Hybrid and Solar Vehicles - Università di Salerno
Hybrid and Solar Vehicles - Università di Salerno
Hybrid and Solar Vehicles - Università di Salerno
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Graphic design: Luciano Statunato - 3D images: Marco Coraggio Whya<br />
International Workshop on<br />
<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong><br />
November 6, 2006 - University of <strong>Salerno</strong>, Italy<br />
<strong>Hybrid</strong><br />
<strong>Solar</strong><br />
Vehicle?<br />
Provincia <strong>di</strong> <strong>Salerno</strong><br />
www.acsalerno.it<br />
IFAC TC Automotive Control<br />
Energy Conversion Systems<br />
<strong>and</strong> their<br />
Environmental Impact<br />
Istruzione e cultura<br />
Leonardo da Vinci
International Workshop on<br />
<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong><br />
University of <strong>Salerno</strong>, Italy<br />
November 5-6, 2006<br />
www.<strong>di</strong>mec.unisa.it/WHSV<br />
Procee<strong>di</strong>ngs<br />
Copyright © 2006
PREFACE<br />
The growth of mobility has had a positive effect on prosperity <strong>and</strong> quality of life, but its<br />
negative impact on the environment <strong>and</strong> the erosion of non-renewable resources are becoming<br />
more <strong>and</strong> more visible. As a consequence, the attention toward the sustainable mobility is<br />
rapidly increasing, sprea<strong>di</strong>ng from specialists to final users <strong>and</strong> to public opinion. In last<br />
decade, the hybrid electric vehicles have emerged as a valid mid-term solution to reduce fuel<br />
consumption <strong>and</strong> carbon <strong>di</strong>oxide emissions. Their integration with photo-voltaic sources may<br />
give a further contribution toward the mitigation of fossil fuels depletion, global warming <strong>and</strong><br />
climate changes. Despite these promising perspectives, there is a certain lack of systematic<br />
research on the integration of hybrid vehicle technology with solar sources.<br />
This Workshop is de<strong>di</strong>cated to hybrid <strong>and</strong> solar vehicles, with particular emphasis on the<br />
combined use of these two approaches. These procee<strong>di</strong>ngs include 13 papers, from Hungary,<br />
France, Italy, Romania, Spain, Turkey <strong>and</strong> United States. Most of the research presented is<br />
conducted in an academic context, also in cooperation with industry <strong>and</strong> research centres. The<br />
papers cover several aspects of hybrid <strong>and</strong> solar vehicles. The actual trends <strong>and</strong> the<br />
opportunities related to the integration of electric vehicles with photo-voltaic <strong>and</strong>, more<br />
generally, with renewable sources are presented in the first paper. Five papers deal with<br />
modelling, design <strong>and</strong> control of hybrid solar vehicles, also caring for profitableness of such<br />
vehicles. Other five papers concern hybrid electric vehicles: hybri<strong>di</strong>zation of a small vehicle for<br />
urban transportation <strong>and</strong> of a 4WD parallel vehicle, control of super-capacitors, HEV real-time<br />
control <strong>and</strong> performance testing. Other two papers are devoted to photovoltaic sources for<br />
automotive applications, concerning MPPT modelling <strong>and</strong> power interfaces.<br />
I would thank all the Authors for their de<strong>di</strong>cation in preparing excellent technical papers, the<br />
members of Scientific Committee for their cooperation in paper review <strong>and</strong> my colleagues at<br />
the University of <strong>Salerno</strong> for their help in the Workshop organization. We acknowledge the<br />
financial <strong>and</strong> operative support of University of <strong>Salerno</strong> to this Workshop, co-sponsored by the<br />
Technical Committee “Automotive Control” of International Federation of Automatic Control<br />
<strong>and</strong> by SAE Naples Section. We also recognize the significant impulse given to the stu<strong>di</strong>es on<br />
hybrid solar vehicles by the European Community in supporting the Leonardo Project “Engine<br />
Conversion Systems <strong>and</strong> Their Enviromental Impact”, with sponsorship of Automobile Club<br />
<strong>Salerno</strong>, Lombar<strong>di</strong>ni, Saggese <strong>and</strong> Province of <strong>Salerno</strong>.<br />
The Workshop Chair<br />
Gianfranco Rizzo
Chair<br />
Prof. Gianfranco Rizzo, DIMEC, University of <strong>Salerno</strong> (I), grizzo@unisa.it<br />
Scientific Committee<br />
I.Arsie, DIMEC, University of <strong>Salerno</strong> (I)<br />
M.Basset, UHA, Mulhouse (F)<br />
J.Bokor, BUTE, Budapest (HU)<br />
E.Chiappini, University of L’Aquila (I)<br />
G.Gissinger, UHA, Mulhouse (F)<br />
L.Guvenç, ITU, Istanbul (TR)<br />
Y.Guezennec, OSU, Columbus (USA)<br />
L.Guzzella, ETH, Zurich (CH)<br />
I.Ionita, Univ. of Galati (RO)<br />
T.Peter, BUTE, Budapest (HU)<br />
C.Pianese, DIMEC, University of <strong>Salerno</strong> (I)<br />
G.Rizzo, DIMEC, University of <strong>Salerno</strong> (I)<br />
G.Rizzoni, OSU, Columbus, Ohio (USA)<br />
G.Spagnuolo, DIIIE, University of <strong>Salerno</strong> (I)<br />
Organizing Committee<br />
I.Arsie, DIMEC, University of <strong>Salerno</strong> (I)<br />
G.Rizzo, DIMEC, University of <strong>Salerno</strong> (I)<br />
M.Sorrentino, DIMEC, University of <strong>Salerno</strong> (I)<br />
G.Spagnuolo, DIIIE, University of <strong>Salerno</strong>, (I)
CONTENTS<br />
S.E.Letendre<br />
Prometheus Institute for Sustainable Development, Vermont (USA)<br />
USHERING IN AN ERA OF SOLAR-POWERED MOBILITY 1<br />
Zs. Preitl (1), P. Bauer (1), J. Bokor (2)<br />
(1) Budapest University of Technology <strong>and</strong> Economics, Dept. of Transport Automation, Hungary<br />
(2) Computer <strong>and</strong> Automation Research Institute, Budapest, Hungary<br />
FUEL CONSUMPTION OPTIMIZATION FOR HYBRID SOLAR VEHICLE 11<br />
P. Bauer (1), Zs. Preitl (1),P. Gáspár (2), Z. Szabó (2), J. Bokor (2)<br />
(1) Budapest University of Technology <strong>and</strong> Economics, Dept. of Transport Automation, Hungary<br />
(2) Computer <strong>and</strong> Automation Research Institute, Budapest, Hungary<br />
CONTROL ORIENTED MODELLING OF A SERIES HYBRID SOLAR VEHICLE 19<br />
A.Boyali (1), M.Demirci (1), T.Acarman (2), L.Güvenç (1), B.Kiray (3), M.Yil<strong>di</strong>rim (3)<br />
(1) Istanbul Technical University, Mechanical Engineering Dept., Istanbul, Turkey<br />
(2) Galatasaray University, Fac.of Engineering <strong>and</strong> Technology, Istanbul, Turkey<br />
(3) Ford-Otosan, Product Development, R&D Department, Kocaeli, Turkey<br />
SIMULATION PROGRAM AND CONTROLLER DEVELOPMENT FOR A 4WD PARALLEL HEV 27<br />
I.Arsie, R.Di Martino, G.Rizzo, M.Sorrentino<br />
DIMEC, University of <strong>Salerno</strong>, Italy<br />
A MODEL FOR A PROTOTYPE OF HYBRID SOLAR VEHICLE 35<br />
G.Petrone (1), G.Spagnuolo (1), M.Vitelli (2)<br />
(1) DIIIE, University of <strong>Salerno</strong>, Italy<br />
(2) DII, Seconda <strong>Università</strong> <strong>di</strong> Napoli, Aversa (CE), Italy<br />
A MODEL OF MISMATCHED PHOTOVOLTAIC FIELDS FOR SIMULATING HYBRID SOLAR<br />
VEHICLES<br />
I.Ionita, D.Negoita, S.Paraschiv, I.V. Ion<br />
University of Galati "Dunarea de Dos", Romania<br />
THE PROFITABLENESS OF HYBRID SOLAR VEHICLES 49<br />
C.Boccaletti (1), G.Fabbri (1), F.M.Frattale Mascioli (2), E.Santini (1)<br />
(1) Department of Electrical Engineering, University of Rome “La Sapienza”, Italy<br />
(2) Department INFOCOM, University of Rome “La Sapienza”, Italy<br />
TECHNICAL AND ECONOMICAL FEASIBILITY STUDY OF A SMALL HYBRID VEHICLE FOR<br />
URBAN TRANSPORTATION<br />
D.Paire (1), M.Becherif (2), A.Miraoui (1)<br />
(1) L2ES, UTBM, Belfort (cedex) 90010, France<br />
(2) SeT, UTBM, Belfort (cedex) 90010, France<br />
PASSIVITY-BASED CONTROL OF HYBRID SOURCES APPLIED TO A TRACTION SYSTEM 63<br />
G.Rousseau (1,2), D.Sinoquet (1), P.Rouchon (2)<br />
(1) Institut Français du Pétrole, 92852 Rueil Malmaison, France<br />
(2) Centre Automatique et Systèmes, École des Mines de Paris, Paris, France<br />
HYBRID ELECTRICAL VEHICLES: FROM OPTIMISATION TOWARD REAL-TIME CONTROL<br />
STRATEGIES<br />
N.Caccavo, G.Carbone, L.Mangialar<strong>di</strong>, L.Soria<br />
Dipartimento <strong>di</strong> Ingegneria Meccanica e Gestionale, Politecnico <strong>di</strong> Bari, Italy<br />
PERFORMANCE TESTING OF HYBRID VEHICLES IN BARI DOWNTOWN 79<br />
M. Cacciato, A. Consoli, G. Scarcella, A. Testa<br />
Dipartimento <strong>di</strong> Ingegneria Elettrica Elettronica e dei Sistemi, Catania, Italy<br />
HYBRID VEHICLES WITH ELECTRICAL MULTI ENERGY UNITS 87<br />
A.Cid-Pastor (1,3), L.Martínez-Salamero (2), C.Alonso (1), G.Schweitz (3), R.Leyva (2)<br />
(1) LAAS-CNRS, Laboratoire d’Analyse et des Architectures des Systèmes, Toulouse, France<br />
(2) ETSE Universitat Rovira i Virgili / Dept. Eng. Electrònica, Elèctrica i Automàtica, Tarragona, Spain<br />
(3) EDF R&D / LME Department, Moret sur Loing, France<br />
IMPEDANCE MATCHING FOR PV GENERATOR 93<br />
43<br />
57<br />
71
USHERING IN AN ERA OF SOLAR-POWERED MOBILITY<br />
Steven E. Letendre, Ph.D.<br />
Green Mountain College, Poultney, VT &<br />
The Prometheus Institute for Sustainable Development, Cambridge, MA, USA<br />
Letendre@vermontel.net<br />
Abstract: Modern mobility, for both humans <strong>and</strong> commo<strong>di</strong>ties, relies almost exclusively<br />
on fuels derived from petroleum. At the same time the world is experiencing soaring<br />
dem<strong>and</strong> for mobility, environmental <strong>and</strong> resource constraints have become increasingly<br />
acute. This article <strong>di</strong>scusses the role that electric drive, initially in the form of hybrid<br />
electric vehicles, can play in addressing the mobility challenge. This article <strong>di</strong>scusses the<br />
opportunity that electric drive vehicles create to use solar <strong>and</strong> wind power for<br />
transportation. The potential of the emerging vehicle integrated PV concept is <strong>di</strong>scussed,<br />
along with the importance of connecting cars to the electric grid.<br />
Keywords: electric vehicles, solar energy, renewable energy systems, electric power<br />
systems<br />
1. MOBILITY IN THE 21 ST CENTURY<br />
Human progress is tied to advances in mobility.<br />
Societies adept at harnessing technology to reduce<br />
the travel times to <strong>di</strong>stant l<strong>and</strong>s successfully gained<br />
access to new resources, allowing wealth creation<br />
opportunities beyond which local resources allowed.<br />
The process accelerated dramatically as fossil fuels<br />
were employed to provide even greater opportunities<br />
to move people <strong>and</strong> commo<strong>di</strong>ties across great<br />
<strong>di</strong>stances.<br />
Today, mobility is a commo<strong>di</strong>ty for which dem<strong>and</strong> is<br />
linked closely to income. Specifically, increases in<br />
dem<strong>and</strong> for highway travel <strong>and</strong> air travel in a country<br />
tracks closely growth in national income. Figure 1<br />
provides data on per capita vehicle miles travelled<br />
(VMT) <strong>and</strong> per capital air travel from 1960 to 2004<br />
in the US. During this timeframe per capita income<br />
grew from $13,800 to $38,856 while per capita VMT<br />
more than doubled <strong>and</strong> per capita domestic air travel<br />
quadrupled. Based on the experiences in the US, per<br />
capita VMT took approximately 30 years to double,<br />
while per capita domestic miles flown doubled in just<br />
ten years.<br />
per capita VMT<br />
12,000<br />
10,000<br />
8,000<br />
6,000<br />
4,000<br />
2,000<br />
-<br />
1960 1965 1970 1975 1980 1985 1990 1995 2000 2004<br />
Year<br />
Per Capita VMT<br />
Per Capita Miles Flown<br />
(domestic)<br />
Fig. 1. Mobility trends in the US: Per capita<br />
vehicles miles travelled <strong>and</strong> per capita domestic<br />
air travel, 1960 to 2004 (Sources: US Bureau of<br />
Economic Statistics <strong>and</strong> the US Bureau of<br />
Transportation Statistics)<br />
As incomes in the developing world rise, dem<strong>and</strong> for<br />
mobility likewise increases in these regions. Myer<br />
<strong>and</strong> Kent (2003) in their book New consumers: The<br />
influence of affluence on the environment highlight<br />
the rapid increase in dem<strong>and</strong> for personal<br />
automobiles occurring in the developing world <strong>and</strong> in<br />
countries as a new consumer class emerges. They<br />
argue that over 1 billion of these new consumers will<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 1<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
per capita air travel
soon have an aggregate spen<strong>di</strong>ng capacity, in<br />
purchasing power parity terms, to match that of the<br />
US. Recent data suggests that China is rapidly<br />
exp<strong>and</strong>ing its automobile manufacturing capabilities;<br />
annual passenger production grew from 100,000<br />
vehicles in 1991 to 2.3 million in 2004—a 28 fold<br />
increase (Worldwatch Institute, 2006).<br />
We have reached an apex in global mobility. The<br />
shear volume <strong>and</strong> pace of movement, of both humans<br />
<strong>and</strong> commo<strong>di</strong>ties, on this planet is incomprehensible.<br />
The 3.7 trillion passenger-kilometers of air travel in<br />
2005 equals over four <strong>and</strong> a half million round trips<br />
from the Earth to the Moon (ICAO, 2005).<br />
What made this level of mobility possible, <strong>and</strong> how<br />
much longer can it be sustained? This critical<br />
question is addressed in the next section of the<br />
article.<br />
1.1 Petroleum <strong>and</strong> transportation: resource<br />
constraints, the environment, & supply risks<br />
Petroleum-derived fuels, such as gasoline for<br />
vehicles <strong>and</strong> jet fuel for modern aircraft, provide<br />
over 97% of primary energy for transportation. Of<br />
the 80 million barrels used globally each day in<br />
2003, approximately one half are consumed for<br />
transportation. The US Department of Energy’s<br />
Energy Information Administrations (EIA) pre<strong>di</strong>cts<br />
that global oil consumption will reach 118 million<br />
barrels per day by 2030 (EIA, 2006). In sum,<br />
transportation is entirely dependant on a single<br />
source of energy—petroleum—<strong>and</strong> its consumption<br />
for transportation purposes is pre<strong>di</strong>cted to rise by<br />
47% in twenty-five years. Most of this increase will<br />
come from rising dem<strong>and</strong> for transportation in non-<br />
OECD countries (EIA, 2006).<br />
The state of modern transportation systems is<br />
extremely precarious. Relying exclusively on<br />
petroleum as a source of energy for transportation<br />
creates significant risks, the most important of which<br />
is resource limits. Volumes have been written about<br />
the so called peak oil phenomenon, which suggests<br />
that global oil production peaks <strong>and</strong> subsequently<br />
enters a prolonged period of decline. While oil does<br />
not “run out” many pre<strong>di</strong>ct that prices rise<br />
dramatically in the face of rising dem<strong>and</strong> <strong>and</strong><br />
declining production (Simmons, 2005). While the<br />
timing of peak oil is the subject of debate, it’s<br />
generally accepted that it will occur within the first<br />
half of this century.<br />
The use of petroleum for transportation is a factor<br />
linked to global climate change. The combustion of<br />
fuels for transportation causes carbon <strong>di</strong>oxide<br />
emissions, the primary pollutant contributing to<br />
global warming, into the atmosphere.<br />
Approximately 25% of global emissions of carbon<br />
<strong>di</strong>oxide come from the transport sector. In ad<strong>di</strong>tion,<br />
transport related emissions are one of the fastest<br />
growing categories, which is likely to increase the<br />
share of total carbon emissions coming from the<br />
transport sector.<br />
A number of recent scientific stu<strong>di</strong>es suggest that<br />
global climate change is occurring more rapidly than<br />
scientists pre<strong>di</strong>cted <strong>and</strong> is already having negative<br />
impact on ecosystems across the globe.<br />
Governments <strong>and</strong> non-governmental organizations<br />
worldwide are calling for dramatic reductions in<br />
carbon <strong>di</strong>oxide emissions to minimize further<br />
warming of the Earth <strong>and</strong> the associated<br />
consequences of rising sea levels, more severe<br />
weather patterns, <strong>and</strong> negative ecosystem impacts.<br />
Clearly, efforts are needed to reduce the transportrelated<br />
emissions of carbon; this can only be<br />
accomplished by either reducing the amount of<br />
travel, increasing the efficiency of the vehicle fleet,<br />
shifting toward alternative fuels, or some<br />
combination there of.<br />
Supply risks are an ad<strong>di</strong>tional concern linked to the<br />
transport sector’s exclusive reliance on oil as a<br />
primary energy source. Roughly one-third of global<br />
oil production comes from the politically volatile<br />
Middle East (EIA, 2006). Furthermore, this region is<br />
home to the largest known oil reserves, thus the<br />
region will become increasingly important as a<br />
global supplier. The region is currently enmeshed in<br />
several armed conflicts, inclu<strong>di</strong>ng the conflict<br />
between the US <strong>and</strong> Iraq. Terrorist attacks on key<br />
ports <strong>and</strong> escalating regional violence could cause<br />
significant supply shocks.<br />
2. TOWARD SUSTAINABLE MOBILITY<br />
The scope of the mobility challenge is daunting. The<br />
issue must be addressed on multiple fronts, from<br />
smart planning to reduce the need for travel by<br />
automobiles to the development of new vehicle<br />
technologies.<br />
The remainder of this article focuses specifically on<br />
options to reduce the light vehicle fleet’s dependence<br />
on petroleum-derived fuel sources. This is achieved<br />
through either improving fuel economy <strong>and</strong>/or using<br />
alternative fuels. Progress has been made in these<br />
areas, but virtually all vehicles commercially<br />
available today run primarily on either gasoline or<br />
<strong>di</strong>esel fuel.<br />
In the US, the primary mechanism for regulating<br />
vehicle fuel economy is the Corporate Average Fuel<br />
Economy (CAFE) st<strong>and</strong>ard, established at the<br />
national level. These st<strong>and</strong>ards remain unchanged<br />
since 1985 at 27.5 miles per gallon (mpg). Europe is<br />
further along in addressing the mobility challenge<br />
with more developed mass transit systems <strong>and</strong> a<br />
much more efficient light vehicle fleet than that<br />
found in the US.<br />
The search for viable alternative fuels has focused on<br />
biofuels, with interest in biofuels surging in recent<br />
years. Brazil is often held up as a successful<br />
example of large-scale biofuel development, meeting<br />
20% of its transport fuel requirements with ethanol<br />
derived from sugar cane. The development of flexfuel<br />
vehicles in the US is gaining momentum, which<br />
provides a vehicle owner a choice of energy options<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 2
to meet their transportation needs. For example,<br />
some automobile manufacturers are buil<strong>di</strong>ng vehicles<br />
that operate on biofuel blends like E85—a blend of<br />
85% ethanol <strong>and</strong> 15% gasoline.<br />
Biofuels offer the potential to reduce our dependence<br />
on gasoline for the light vehicle fleet, but the<br />
potential is limited. There is much debate about the<br />
energy balance of biofuels <strong>and</strong> the appropriateness of<br />
using arable l<strong>and</strong> to produce energy crops as apposed<br />
to food. It is unlikely that biofuels will emerge as a<br />
replacement for gasoline as a transport fuel, although<br />
they could serve to <strong>di</strong>splace a small portion of<br />
gasoline <strong>and</strong> <strong>di</strong>esel fuel for the light vehicle fleet.<br />
Much effort is being <strong>di</strong>rected at producing fuel cells<br />
for mobile applications, fuelled with onboard<br />
compressed hydrogen. Fuel cell vehicles running on<br />
compressed hydrogen are viewed by some as the<br />
ultimate means to achieve sustainable mobility. In<br />
recent years, however, some have questioned the<br />
over emphasis on research <strong>and</strong> development in to<br />
fuel cell vehicles <strong>and</strong> their potential to reduce carbon<br />
emissions in the short-term. It is becoming<br />
increasingly clear that hydrogen-powered fuel cells<br />
vehicles face a number of technical <strong>and</strong> economic<br />
challenges that will likely take decades to address<br />
(Morris, 2003).<br />
In a 2004 report prepared by the US-based Center for<br />
Energy <strong>and</strong> Climate Solutions for the National<br />
Commission on Energy Policy concluded, “We<br />
believe that the most plausible vehicle of the<br />
future is a plug-in hybrid running on a<br />
combination of low-carbon electricity <strong>and</strong> a lowcarbon<br />
biomass-derived fuel.” (Center for Energy<br />
<strong>and</strong> Climate Solutions, 2004)<br />
2.1 The hybrid electric vehicle revolution<br />
<strong>Hybrid</strong> electric vehicles (HEV), using both an<br />
internal combustion engine <strong>and</strong> electric motor,<br />
achieve dramatic improvements in fuel economy.<br />
Commercially available HEVs boast fuel economy<br />
ratings of over 50 mpg. For example, the most<br />
popular hybrid, the Toyota Prius, achieves a fuel<br />
economy rating of 60 mpg highway <strong>and</strong> 51 mpg city.<br />
Consumers now have several HEV options to choose<br />
from, <strong>and</strong> their popularity among the car-buying<br />
public is increasing. Virtually every major<br />
automobile manufacturer is manufacturing, or plans<br />
to in the near future, HEVs. In 2005, HEVs reached<br />
1.2% of new cars sold in the US, more than doubling<br />
the number sold in the prior year. Toyota is the<br />
lea<strong>di</strong>ng manufacturer of HEVs, globally selling over<br />
50% of all hybrids purchased in the US in 2005.<br />
The evolution of HEVs to allow charging from the<br />
electric grid, so called plug-in hybrids (PHEV), is<br />
assumed by many to be desirable—some may argue<br />
inevitable. Ultimately, the economics of <strong>di</strong>splacing<br />
gasoline with electricity should drive consumer<br />
dem<strong>and</strong> for PHEVs. The cost of electricity to drive a<br />
vehicle the same <strong>di</strong>stance as one gallon of gasoline is<br />
equal to approximately $1—or even less if off-peak<br />
electricity prices are assumed (Denholm <strong>and</strong> Short,<br />
2006). Furthermore, as <strong>di</strong>scussed later in this article,<br />
PHEVs could potentially generate revenue for the<br />
vehicle owner by provi<strong>di</strong>ng grid support services.<br />
Combined, these value propositions could serve to<br />
usher in an era of advanced vehicles with dramatic<br />
reductions in gasoline use <strong>and</strong> tailpipe emissions.<br />
A growing, national movement to bring PHEVs to<br />
the market has emerged in the US, bolstered by the<br />
undeniable economic <strong>and</strong> national security benefits<br />
that result from <strong>di</strong>splacing gasoline with electricity.<br />
One highly-visible, grass-roots campaign, called<br />
Plug-In Partners, seeks to demonstrate to the major<br />
automobile manufacturers that a national market<br />
exists for flexible-fuel PHEVs; dozens of businesses,<br />
utilities, municipal governments, <strong>and</strong> environmental<br />
groups have joined the Plug-In Partners campaign.<br />
While there are no commercially available PHEVs<br />
on the market, a number of prototypes have been<br />
built <strong>and</strong> tested. The most established PHEV<br />
program is housed at the University of California<br />
Davis, where Professor Andrew Frank works with<br />
students designing <strong>and</strong> buil<strong>di</strong>ng prototype PHEVs. A<br />
second development project involves collaboration<br />
between the Electric Power Research Institute <strong>and</strong><br />
DaimlerChrysler. They produced, <strong>and</strong> are in the<br />
process of testing, several prototype plug-in hybrid<br />
vans using the Sprinter platform. Two start-up firms<br />
plan to offer conversion kits for current generation<br />
hybrid electric vehicles to allow grid charging of the<br />
on-board battery pack. These conversions kits offer<br />
the potential to almost double an HEV’s fuel<br />
efficiency rating to 100+ miles per gallon by<br />
increasing the size of the battery storage system <strong>and</strong><br />
installing the hardware <strong>and</strong> controls to allow<br />
charging from the electric grid.<br />
3. HYBRIDS AND RENEWABLES: EXPLORING<br />
THE POTENTIAL<br />
As the vehicle fleet moves toward electric drive,<br />
initially in the form of HEVs, the opportunity for<br />
renewables, beyond biofuels, to serve as an energy<br />
source for the transport sector emerges. This<br />
opportunity is greatly enhance when vehicles connect<br />
to the grid to charge an onboard battery pack. The<br />
remainder of this article explores this opportunity<br />
from the emerging vehicle integrated concept (VIPV)<br />
to the role that wind can play in powering gridconnected<br />
cars.<br />
<strong>Hybrid</strong>s electric vehicles with the capability to<br />
recharge from the electric grid dramatically reduce<br />
the needed liquid fuels for transportation. Stu<strong>di</strong>es<br />
have found that most vehicles could meet the vast<br />
majority of their daily commute with a PHEV<br />
designed with a 40 mile all electric range. Thus,<br />
PHEVs can exploit wind <strong>and</strong> solar as a fuel source<br />
<strong>and</strong> at the same time dramatically reduce liquid fuel<br />
requirements. It becomes more realistic for biofuels<br />
to meet the lower liquid fuel requirements needed as<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 3
the vehicle fleet relies to a greater degree on<br />
electricity.<br />
3.1 The <strong>Solar</strong> <strong>Hybrid</strong> Electric Vehicle<br />
In 2003, the author presented the vehicle integrated<br />
photovoltaic (VIPV) concept to an American<br />
au<strong>di</strong>ence at the annual meeting of the American<br />
<strong>Solar</strong> Energy Society. The paper titled, Vehicle<br />
integrated PV: A clean <strong>and</strong> secure fuel for hybrid<br />
electric vehicles argued that HEVs create an<br />
opportunity for PV to serve as an energy source for<br />
the transport sector.<br />
Until recently, PV has not been considered a viable<br />
energy source for vehicles. Some experiments were<br />
conducted using PV for electric vehicle (EV)<br />
charging, but efforts to commercialize have stalled<br />
due to the perceived lack of market acceptance for<br />
these types of vehicles. Other efforts to deploy PV<br />
for transportation have taken place at a variety of<br />
university research centers, where teams of students<br />
<strong>and</strong> faculty build vehicles powered solely from solar.<br />
These vehicles are designed <strong>and</strong> built to compete in<br />
solar car races such as the World <strong>Solar</strong> Challenge,<br />
which began in Australia in 1987. These vehicles<br />
were never intended for commercial production, the<br />
futuristic look <strong>and</strong> design of these experimental<br />
vehicles would not likely appeal to mass markets.<br />
Since the 2003 conference, the author learned of a<br />
variety of projects to advance the VIPV concept.<br />
Researchers at the University of Queensl<strong>and</strong> in<br />
Australia are developing a commuter hybrid vehicle<br />
with PV integrated in to the body panels. An<br />
engineer in Canada installed a 270 watt solar array<br />
on the roof of his Toyota Prius, increasing the<br />
mileage by approximately 10%. Even the major auto<br />
manufacturers are eyeing the VIPV opportunity, with<br />
both Ford, <strong>and</strong> its close corporate partner Mazda,<br />
<strong>di</strong>splayed hybrid vehicles with modest amounts of<br />
VIPV at recent auto shows. The author produced a<br />
second article on the topic highlighting recent VIPV<br />
activities, which appeared in the May/June 2006<br />
e<strong>di</strong>tion of <strong>Solar</strong> Today.<br />
In October of this year, the French specialty vehicle<br />
manufacturer Venturi Automobiles announced plans<br />
to offer the first commercially available solar hybrid<br />
sports car called the Astrolab. The company also<br />
produces an urban electric commuter vehicle called<br />
the Eclectic. The 3-seater vehicle has solar PV<br />
integrated on to the roof of the vehicle. Venturi<br />
claims that this is the first energy-autonomous<br />
vehicle available to the public.<br />
Pic. 1. PV integrated Toyota Prius, Lapp<br />
Renewables LTD, 2005<br />
Pic. 2. Venturi Automobiles’ Astrolab, the first<br />
commercially available PV integrated hybrid<br />
Pic. 3. Venturi Automobiles’ Eclectic, the first<br />
energy autonomous electric urban commuter<br />
vehilce<br />
Recently, Taiwan’s PV cell manufacturer E-Ton<br />
<strong>Solar</strong> announced a joint venture with several<br />
partners, inclu<strong>di</strong>ng Yulon Nissan Motor Co., Ltd. to<br />
develop PV products for the car market. The joint<br />
venture began with the manufacturing of PV modules<br />
for car sunroofs.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 4
3.2 Design Considerations for <strong>Solar</strong> <strong>Hybrid</strong>s<br />
Given current HEV designs, VIPV could serve to<br />
enhance the overall efficiency of the vehicle, but<br />
only provide a small portion of the vehicle’s energy<br />
requirements. In this context, VIPV is similar to<br />
regenerative breaking, which, through converting the<br />
kinetic energy lost in breaking to electrical energy,<br />
serves to enhance the overall efficiency of an HEV.<br />
A number of design <strong>and</strong> engineering considerations<br />
could serve to increase PV’s role in fuelling a new<br />
generation of solar hybrid vehicles<br />
The key parameters <strong>di</strong>ctating VIPV’s ability to<br />
<strong>di</strong>splace gasoline for transportation are the quantity<br />
of PV in watts integrated on to the body panels <strong>and</strong><br />
the efficiency of the vehicle drivetrain. The amount<br />
of PV that can be integrated on to a vehicle is a<br />
function of the available space <strong>and</strong> the efficiency of<br />
the PV technology deployed. Venturi Automobile’s<br />
Astrolab mentioned above contains 3.6 m 2 of PV<br />
integrated on to the vehicle. Measurements of the<br />
available surface area of a number of conventional<br />
vehicles suggest available surface areas of between<br />
3.5 m 2 to 5.5 m 2 (Letendre et al., 2006). Figure 2<br />
in<strong>di</strong>cates potential PV in watts for three <strong>di</strong>fferent<br />
scenarios of available surface by PV conversion<br />
efficiencies.<br />
watts VIPV<br />
1,200<br />
1,000<br />
800<br />
600<br />
400<br />
200<br />
-<br />
3.5 m2<br />
4.5 m2<br />
5.5 m2<br />
5%<br />
6%<br />
7%<br />
8%<br />
9%<br />
10%<br />
PV Conversion Efficiency<br />
11%<br />
12%<br />
13%<br />
14%<br />
15%<br />
16%<br />
17%<br />
18%<br />
19%<br />
20%<br />
Fig. 2. VIPV watts potential: surface area vs. PV<br />
sunlight to electricity conversion efficiency<br />
As Figure 2 illustrates, the sunlight to conversion<br />
efficiency of the PV technology deployed in VIPV<br />
applications is an important parameter. While flat<br />
plate silicon PV has high conversion efficiencies,<br />
thin film PV may be better suited for VIPV<br />
applications. Again referring back to Venturi<br />
Automobile’s Astrolab, the vehicle uses high<br />
efficiency monocrystaline PV cells to achieve 600<br />
watts of PV on the available 3.6 m 2 of surface area.<br />
Copper in<strong>di</strong>um gallium <strong>di</strong>selenide (CIGS) solar cells,<br />
which are not yet fully commercial, offer both<br />
advantages of flexibility like other thin film PV<br />
technologies, but with much higher conversion<br />
efficiencies. One US company, DayStar<br />
Technologies, is nearing commercial-scale<br />
production of a CIGS PV product on flexible steel.<br />
Generally, the US is lea<strong>di</strong>ng in the development of<br />
the next generation PV technology, which should be<br />
predominantly flexible thin films.<br />
It should be noted that the onboard PV capacity may<br />
not necessarily be constrained by the available<br />
surface area on the vehicle’s body panels, but<br />
flexible PV could be used to design retractable solar<br />
shades that could be deployed when the vehicle is<br />
parked to provide ad<strong>di</strong>tional PV capacity for daytime<br />
charging.<br />
The efficiency of the vehicle drivetrain determines<br />
the number of solar miles obtained from any given<br />
VIPV system. Current hybrids, like the Toyota Prius<br />
have all electric efficiencies in the 156 watt-hours per<br />
kilometer range. Figure 3 illustrates solar miles for a<br />
500 watt VIPV system in a region with an average of<br />
4 sun hours per day for total annual PV generation of<br />
710 kWh.<br />
watt-hours / km<br />
250<br />
200<br />
150<br />
100<br />
50<br />
SUV<br />
Toyota Prius<br />
- 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000<br />
Annual <strong>Solar</strong> Kilometers<br />
Honda Insight<br />
Fig. 3. VIPV watts potential: surface area vs. PV<br />
sunlight to electricity conversion efficiency<br />
Advances in the use of lightweight materials for<br />
vehicles will serve to increase the potential solar<br />
miles delivered from a VIPV system. However, even<br />
today’s commercially available hybrid can benefit<br />
from VIPV. Initial VIPV applications will provide<br />
incremental improvements in vehicle efficiency, but<br />
the future potential is much greater. The Leonardo<br />
Project, sponsored by the European Commission,<br />
aims to train a new generation of engineers in<br />
sustainable transportation focused initially on<br />
designing <strong>and</strong> buil<strong>di</strong>ng a solar hybrid. This project,<br />
<strong>and</strong> other like it, will serve to advance knowledge on<br />
these concepts <strong>and</strong> ultimately achieve advanced<br />
designs that dramatically improve existing<br />
technologies <strong>and</strong> approaches.<br />
Battery storage devices are a critical enabling<br />
technology for the solar hybrid revolution. While<br />
many advances have been made in battery<br />
technology, reductions in price <strong>and</strong> improvements in<br />
performance are needed to produce commercially<br />
viable solar hybrid vehicles.<br />
A promising new battery technology was unveiled at<br />
the September 2006 California Air Resources Board<br />
Zero Emission <strong>Vehicles</strong> Symposium. Navada-based<br />
Altairnano announced a new lithium ion battery<br />
system called NanoSafe, which replaces graphite<br />
as the electrode materials with nano-titanate<br />
materials (www.altairnano.com). The company<br />
claims that this new materials solve the thermal<br />
runaway problem with conventional lithium ion<br />
batteries, <strong>and</strong> offer significant improvements in cycle<br />
life <strong>and</strong> delivers optimum energy/power balance in<br />
the high power region, which is critical for hybrid<br />
<strong>and</strong> electric vehicle applications.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 5
3.3 Plug-In <strong>Hybrid</strong>s Facilitates the Use of Wind for<br />
the Transport Sector<br />
While both conventional HEVs <strong>and</strong> PHEVs can<br />
adopt a VIPV strategy allowing for the use of solar<br />
for transportation, only plug-in hybrids facilitate the<br />
use of wind power for transportation purposes.<br />
Wind power is the fasting growing new source of<br />
power generation world-wide. In the US alone the<br />
American Wind Energy Association estimates that<br />
over 3,000 MW of new wind will go on line in 2006.<br />
Globally, estimates of installed wind power capacity<br />
reached 60,000 MW in 2005 (Worldwatch Institute,<br />
2006). Wind power is a clean <strong>and</strong> renewable source<br />
of power generation that will continue to exp<strong>and</strong> in<br />
the coming years.<br />
The intermittent nature of wind power creates<br />
challenges for developers seeking to integrate wind<br />
into electric grids <strong>and</strong> wholesale markets. At low<br />
wind power penetration rates intermittency is less of<br />
an issue; however, as wind plays an increasingly<br />
important role in the global supply mix,<br />
intermittency will need to be addressed. The<br />
variability of output from wind farms creates<br />
challenges given the existing electric industry<br />
structure, which is characterized by scheduled flows<br />
of power from sources to sinks. The cost <strong>and</strong><br />
environmental characteristics, however, are<br />
sufficiently compelling that regulations have been<br />
devised to facilitate wind power integration in to the<br />
electric supply mix.<br />
The variability of wind power can be understood in<br />
<strong>di</strong>screte categories based on increasingly longer time<br />
intervals that characterize the market strategy that is<br />
needed to manage the variability as more <strong>and</strong> more<br />
wind appears on the electric network. These<br />
categories are:<br />
• Minute to hour variability, addressed<br />
through regulation markets, intra-hour<br />
adjustments, or spinning reserves.<br />
• Hour to day, addressed through operating<br />
reserves (spinning <strong>and</strong> non-spinning<br />
reserves)<br />
• 1-4 days, <strong>di</strong>spersion of wind resources with<br />
transmission, operating reserves, load<br />
management, <strong>and</strong> de<strong>di</strong>cated storage<br />
(Kempton <strong>and</strong> Tomic, 2005a)<br />
Recent analyses suggest that the emergence of<br />
PHEVs <strong>and</strong> other electric vehicles could serve to<br />
address the intermittency challenge associated with<br />
wind <strong>and</strong> other intermittent resources like solar<br />
(Letendre et al., 2002; Kempton <strong>and</strong> Tomic, 2005a,<br />
<strong>and</strong> Denholm <strong>and</strong> Short, 2006). In one of these<br />
stu<strong>di</strong>es Kempton <strong>and</strong> Tomic (2005a) calculate that<br />
that electric vehicles with onboard battery storage<br />
<strong>and</strong> bi-<strong>di</strong>rectional power flows could stabilize largescale<br />
(one-half of US electricity) wind power with<br />
3% of the fleet de<strong>di</strong>cated to regulation for wind, plus<br />
8–38% of the fleet provi<strong>di</strong>ng operating reserves or<br />
storage for wind.<br />
At a minimum, the nature of PHEV charging<br />
complements the intermittent nature of wind power.<br />
Given the high periods of non-use of vehicles,<br />
PHEVs represent a new source of load, unlike critical<br />
loads like computers <strong>and</strong> other information<br />
technologies, which doe not require a constant flow<br />
of power for re-charge. The charging of PHEVs can<br />
be modulated as the power production from a wind<br />
farm varies. This serves to address the first tear of<br />
intermittency (variability) described earlier. I<br />
envision new power contracts between PHEV owners<br />
<strong>and</strong> developers of wind farms. The complementary<br />
nature of wind power <strong>and</strong> PHEVs creates an<br />
opportunity to further enhance the environmental<br />
character of PHEVs through wind power charging.<br />
To address the second <strong>and</strong> third tiers of wind power<br />
variability described earlier, PHEVs would require<br />
reverse flow capabilities. This concept has become<br />
widely known as the vehicle to grid (V2G) concept,<br />
which is covered extensively in the next section of<br />
this article. Millions of PHEVs connected to the<br />
electric grid would represent a very large aggregate<br />
energy storage resource. Figure 4 in<strong>di</strong>cates the<br />
amount of storage that would be connected to the<br />
grid for PHEVs with various electric only ranges<br />
(from 20 to 60 miles) by the number of vehicles.<br />
Even at small penetration rates in the new car market<br />
PHEVs could offer a significant storage capacity to<br />
address wind power’s longer duration variability.<br />
MWh Storage Potential<br />
400,000<br />
350,000<br />
300,000<br />
250,000<br />
200,000<br />
150,000<br />
100,000<br />
50,000<br />
PHEV60<br />
PHEV40<br />
PHEV30<br />
PHEV20<br />
0<br />
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 8.5 9 9.5 10<br />
Millions of V2G <strong>Vehicles</strong><br />
Fig. 4. PHEV energy storage potential<br />
It’s quite possible that VIPV, wind power charging,<br />
<strong>and</strong> ethanol or bio<strong>di</strong>esel could create the first mass<br />
market, mobility solution that is 100% renewable.<br />
This mobility system becomes even more attractive<br />
when understood in the context of the emerging<br />
vehicle to grid concept. Next, I turn to this topic <strong>and</strong><br />
describe the benefits that are possible as the transport<br />
<strong>and</strong> electric power sectors converge.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 6
4. V2G: INTEGRATING THE TRANSPORT AND<br />
ELECTRIC POWER SECTORS<br />
As the vehicle fleet moves toward electric drive,<br />
initially in the form of HEVs, interesting synergies<br />
can be exploited between the transport <strong>and</strong> the<br />
electric power sectors when a bi-<strong>di</strong>rectional grid<br />
interface is built. In aggregate, grid-connected cars<br />
would represent a potentially large <strong>and</strong> highly<br />
reliable power resource to the electric power sector.<br />
This opportunity was first explored by Kempton <strong>and</strong><br />
Letendre in a 1997 article published in<br />
Transportation Research-D.<br />
The light vehicle fleet <strong>and</strong> the electric power system<br />
represent two massive energy conversion systems,<br />
which evolved in isolation from each other over the<br />
past century. The electric power system relies on<br />
thous<strong>and</strong>s of generating units which convert stored<br />
energy (chemical [coal, natural gas, oil], mechanical<br />
[hydro <strong>and</strong> wind], <strong>and</strong> nuclear) in to alternating<br />
current that flows across a massive interconnected<br />
transmission <strong>and</strong> <strong>di</strong>stribution grid to final end users.<br />
In contrast, the light vehicle fleet coverts<br />
petrochemical energy to rotary motion <strong>and</strong> then to<br />
travel. A massive petroleum, refining, <strong>and</strong> transport<br />
infrastructure exists to support the light vehicle<br />
fleet’s energy needs.<br />
The electric power industry is unique in that the<br />
product, electricity, is produced <strong>and</strong> consumed at the<br />
same time. There is virtually no storage in the<br />
system; except for pumped hydro in select locations.<br />
Grid operators must continuously match supply <strong>and</strong><br />
dem<strong>and</strong> by turning on <strong>and</strong> off generators in response<br />
to dem<strong>and</strong>. In contrast the light vehicle fleet requires<br />
storage, given that its fuel must be mobile <strong>and</strong> thus is<br />
carried onboard in a storage container. As the light<br />
vehicle fleet migrates toward electric drive, storage<br />
energy in onboard batteries serves to supplement the<br />
stored energy in the vehicle’s fuel tank.<br />
Electric generators are designed for high duty cycles,<br />
in the US average utilization rates of the nation’s<br />
generating assets reaches 60%. In contrast, as<br />
mentioned above, vehicles are in use approximately<br />
5% of the time. While electric generators can take<br />
minutes or hours to deliver power to the grid, electric<br />
drive vehicles could deliver power to the grid<br />
virtually instantaneously.<br />
In aggregate these complementary characteristics of<br />
the electric power sector <strong>and</strong> the light vehicle fleet<br />
offer a compelling reason to evaluate the integration<br />
of these systems as vehicle technology migrates<br />
toward electric drive. Through a bi-<strong>di</strong>rectional<br />
interface, grid-connected cars could deliver power<br />
when called upon by a central grid operator. Figure<br />
5 illustrates schematically the vehicle to grid (V2G)<br />
concept. Advances <strong>and</strong> cost reductions in wireless<br />
communications would allow a central operator to<br />
<strong>di</strong>spatch stored energy in vehicles upon dem<strong>and</strong>. In<br />
Figure 5 the Independent System Operator (ISO) is<br />
delivering a <strong>di</strong>spatch signal to those vehicles<br />
connected to the grid <strong>and</strong> prepared to deliver power<br />
at a moments notice.<br />
Fig. 5. Schematic of vehicle to grid concept<br />
(Kempton <strong>and</strong> Tomic, 2005a)<br />
Even at small fractions of the vehicle fleet, electric<br />
drive vehicles could represent a very large power<br />
resource. At 10 kW per vehicle, one million vehicles<br />
represent 10,000 MW of available V2G power; the<br />
current global vehicle fleet is estimated to be over<br />
600 million vehicles (Worldwatch Institute, 2006).<br />
4.1 V2G Research Fin<strong>di</strong>ng<br />
The author knows of just one V2G demonstration<br />
project (Brooks, 2002). The demonstration project<br />
was conducted by a California-based electric vehicle<br />
development company AC Propulsion, in<br />
conjunction with the California Independent System<br />
Operator (ISO). AC Propulsion produces the only<br />
V2G capable electric vehicle drivetrain. For the<br />
demonstration project a Volkswagen Beetle was<br />
converted to a pure electric vehicle outfitted with AC<br />
Propulsion’s bi-<strong>di</strong>rectional charger <strong>and</strong> a<br />
communication link with the California ISO. They<br />
successfully demonstrated the remote <strong>di</strong>spatch of<br />
power from a parked electric vehicle in response to a<br />
signal from the ISO.<br />
Most of the research to date on V2G involves<br />
modelling <strong>and</strong> economic analyses. One<br />
comprehensive study, for which the author was<br />
involved, was funded by the California Air<br />
Resources Board. Although no technical barriers<br />
were <strong>di</strong>scovered in the research, a number of key<br />
issues were identified that bear on the economic<br />
value of V2G power services.<br />
Research on this topic suggests that V2G capable<br />
cars are best suited to provide grid services that<br />
require a rapid response, but our used for a short<br />
duration. The limited onboard energy storage of an<br />
electric drive vehicle is not suited for provi<strong>di</strong>ng baseload<br />
power. The most promising markets for V2G<br />
power fall under the hea<strong>di</strong>ng of ancillary services—<br />
services purchased by grid operators to maintain<br />
system reliability. The two most valuable ancillary<br />
services in the US are for regulation (frequency<br />
response) <strong>and</strong> spinning reserves. Economic analyses<br />
demonstrate that a single vehicle can generate<br />
hundreds of dollar annually provi<strong>di</strong>ng these services<br />
(Letendre <strong>and</strong> Kempton, 2002).<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 7
A second important issue for V2G capable cars,<br />
which determines the potential revenue from<br />
provi<strong>di</strong>ng grid services, is the power output that can<br />
be sustained by a vehicle provi<strong>di</strong>ng ancillary<br />
services. Kempton <strong>and</strong> Tomic (2005b) identify three<br />
key factors that limit the amount of power a gridconnected<br />
car can deliver back to the grid. These<br />
include the on board vehicle electronics, capacity of<br />
the plug circuit, <strong>and</strong> energy storage capacity <strong>and</strong><br />
state of charge when the vehicle is plugged in to<br />
provide grid services.<br />
A PHEV’s vehicle’s power electronics should not<br />
create a bin<strong>di</strong>ng limit on the amount of power that<br />
can be exported to the grid. PHEVs require high<br />
power components for acceleration <strong>and</strong> to optimize<br />
vehicle performance. The electric drivetrain<br />
developed <strong>and</strong> manufactured by AC Propulsion<br />
mentioned earlier provides 80 amps in either<br />
<strong>di</strong>rection, allowing 19.2 kW of power output. Thus,<br />
the critical factors <strong>di</strong>ctating the reverse power<br />
potential come down to the capacity of the plug<br />
circuit <strong>and</strong> the size <strong>and</strong> state of charge of the PHEV’s<br />
battery pack.<br />
Given the evidence on the V2G potential today, the<br />
next logical step would be a large-scale<br />
demonstration project. A fleet of say 100 electric<br />
drive vehicles equipped with a bi-<strong>di</strong>rectional charger<br />
could serve to resolve some issues that would give<br />
the private sector more confidence in pursuing the<br />
V2G business opportunity. In the end, the revenue<br />
that V2G could generate would help to overcome the<br />
price premium for the first-generation plug-in<br />
hybrids or pure electric vehicles, thus ushering in a<br />
new era of clean, flexible fuel vehicles.<br />
As experience is gained <strong>and</strong> the price of electric<br />
drive vehicles declines, their use in provi<strong>di</strong>ng peak<br />
power <strong>and</strong> storage for intermittent renewables is<br />
more likely. Furthermore, an increasingly fleet of<br />
V2G capable vehicles could eventually enhance the<br />
overall reliability of the grid <strong>and</strong> support a more<br />
environmentally sound electric supply mix.<br />
5. CONCLUSION<br />
As we enter the early stages of the 21 st Century,<br />
society has reached an apex in mobility. The global<br />
economy is poised precariously on continues flows<br />
of people <strong>and</strong> goods, made possible by an abundant<br />
<strong>and</strong> cheap source of energy—oil! Recent events<br />
suggest that this critical resource is no longer<br />
abundant <strong>and</strong> cheap. In 2006, petroleum reached<br />
record prices on international exchanges of over $70<br />
per barrel. Some of the world’s most renowned<br />
petroleum geologists are warning that we are quickly<br />
approaching the point at which we have extracted<br />
approximately one half of the existing oil reserves<br />
buried deep in the Earth crust—the so called peak oil<br />
event.<br />
These, <strong>and</strong> other critical geopolitical events, suggest<br />
that society must rapidly pursue the development of<br />
alternative means of transportation to maintain even<br />
a portion of the mobility we have come to rely upon<br />
in this modern ear. It’s becoming increasingly clear<br />
that electric drive will play a central role in the future<br />
vehicle fleet. Already, today hybrid electric vehicles<br />
(HEVs) have gained commercial success. Many<br />
groups are actively pursuing the logical evolution of<br />
HEVs to allow charging from the electric grid.<br />
Others are focused on hydrogen as the primary<br />
energy carry for transportation, fuelling a future fleet<br />
of fuel cell vehicles. Regardless of the technology<br />
that dominates the future, vehicle will rely<br />
increasingly on electric drive <strong>and</strong> contain<br />
significantly more onboard battery storage than<br />
today’s fleet of internal combustion engines.<br />
This new era of electric drive vehicles allows for<br />
renewables, beyond biofuels, to serve as an energy<br />
source for the light vehicle fleet. Vehicle integrated<br />
PV <strong>and</strong> grid-connected cars charging from wind<br />
power become real possibilities as hybrid electric<br />
vehicles emerge as viable alternatives to internal<br />
combustion vehicles. There is tremendous<br />
momentum in this <strong>di</strong>rection as research<br />
organizations, governments, <strong>and</strong> private industry<br />
seek to solve our immanent mobility crisis. A French<br />
specialty automobile company plans to offer the first<br />
commercial solar hybrid to consumers. E-Ton <strong>Solar</strong>,<br />
a major PV manufacturer, has entered a joint venture<br />
to develop products specifically for the car market.<br />
Finally, the V2G concept is the ultimate vision<br />
whereby the transport <strong>and</strong> electric power sector<br />
converge <strong>and</strong> reap tremendous efficiencies while<br />
improving reliability, reducing pollution, <strong>and</strong><br />
delivering greater energy security to those nations<br />
with the foresight to seize this opportunity.<br />
REFERENCES<br />
Brooks, A. (2002). Vehicle-to-grid demonstration<br />
project: Grid regulation ancillary service with a<br />
battery electric vehicle. Report to the California<br />
Air Resources Board.<br />
The Center for Energy <strong>and</strong> Climate Solutions. (June<br />
2004) The car <strong>and</strong> fuel of the future: A<br />
technology <strong>and</strong> policy overview, Prepared for the<br />
National Commission on Energy Policy,<br />
Washington, DC.<br />
Energy Information Administration (EIA), US<br />
Department of Energy. (2006). International<br />
energy outlook 2006, Washington, DC.<br />
International Civilian Aviation Organization (ICAO).<br />
(28 July 2005). World air passenger traffic to<br />
continue to exp<strong>and</strong> through to 2007, press<br />
release, Montreal.<br />
Kempton, W <strong>and</strong> J. Tomic. (2005a). V2G<br />
implementation: From stabilizing the grid to<br />
supporting large-scale renewable energy. J.<br />
Power Sources, 144, 280-294.<br />
Kempton, W <strong>and</strong> J. Tomic. (2005b). Vehicle to grid<br />
fundamentals: Calculating capacity <strong>and</strong> net<br />
revenue. J. Power Sources 144, 1, 268-279.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 8
Kempton, W., J Tomic, S. Letendre, A. Brooks, <strong>and</strong><br />
T. Lipman. (2001). Electric drive vehiclesbattery,<br />
hybrid, <strong>and</strong> fuel cell-as resources for<br />
<strong>di</strong>stributed electric power in California,<br />
University of California Davis, ITS-RR-01-03.<br />
Kempton, W., <strong>and</strong> S. Letendre. (1997). Electric<br />
vehicles as a new power source for electric<br />
utilities. Transportation Research-D, 2, 157-<br />
175.<br />
Letendre, S. R. Perez, <strong>and</strong> C. Herig. (May/June<br />
2006). <strong>Solar</strong> vehicles at last?. <strong>Solar</strong> Today, Vol.<br />
20, No. 3, 26-29.<br />
Letendre, S., R. Perez, <strong>and</strong> C. Herig. (2003). Vehicle<br />
integrated PV: a clean <strong>and</strong> secure fuel for hybrid<br />
electric vehicles. Procee<strong>di</strong>ngs of the 2003<br />
American <strong>Solar</strong> Energy Society Annual<br />
Conference, Boulder, CO.<br />
Letendre, S <strong>and</strong> W. Kempton. (2002). V2G: a new<br />
model for power?. Public Utilities Fortnightly,<br />
140, 16-26.<br />
Letendre, S., R. Perez, <strong>and</strong> C. Herig. (2002). Batterypowered,<br />
electric-drive vehicles provi<strong>di</strong>ng buffer<br />
storage for PV capacity value. Procee<strong>di</strong>ngs of<br />
the 2002 American <strong>Solar</strong> Energy Society Annual<br />
Conference, Boulder, CO.<br />
Myers, N. <strong>and</strong> J. Kent. (2004). The new consumers:<br />
The influence of affluence on the environment,<br />
Isl<strong>and</strong> Press, Washington, DC.<br />
Morris, D. (2003). A better way to get from here to<br />
there: A commentary on the hydrogen economy<br />
<strong>and</strong> a proposal for an alternative strategy, The<br />
Institute for Local Self-Reliance, Minneapolis,<br />
MN.<br />
Simmons, M. (2005). Twilight in the desert: The<br />
coming Sau<strong>di</strong> oil shock <strong>and</strong> the world economy,<br />
Wiley & Sons, Inc, Hoboken, New Jersey.<br />
Worldwatch Institute. (2006). Vital signs 2006 –<br />
2007: The trends that are shaping our future,<br />
W.W. Norton & Company, Inc., New York, NY.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 9
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 10
FUEL CONSUMPTION OPTIMIZATION FOR HYBRID SOLAR VEHICLE<br />
Zs. Preitl*, P. Bauer*, J. Bokor**<br />
* Budapest University of Technology <strong>and</strong> Economics, Dept. of Transport Automation,<br />
H-1111 Budapest, Bertalan L. u. 2., Hungary<br />
Email: preitl@sch.bme.hu, bauer.peter@mail.bme.hu, bokor@sztaki.hu<br />
** Computer <strong>and</strong> Automation Research Institute,<br />
H-1518 Budapest, Kende u. 13-17, Hungary<br />
Abstract: <strong>Hybrid</strong> electric vehicles (HEVs), having multiple main energy sources, are an<br />
attractive alternative to conventional vehicles. The paper presents a study on minimizing<br />
the energy consumption in a series hybrid solar vehicle (HSV). First a description of the<br />
series HSV is given, after which two control strategies are presented for fuel consumption<br />
optimization. The first control strategy is dynamic programming (DP) which is used to<br />
obtain a global optimum for fuel consumption. The second control algorithm is Model<br />
Pre<strong>di</strong>ctive Control, using the MPC Toolbox of Matlab. Both strategies are tested through<br />
simulations.<br />
Keywords: hybrid solar vehicles (HSV), control strategies, dynamical programming (DP),<br />
Model Pre<strong>di</strong>ctive Control (MPC)<br />
1. INTRODUCTION<br />
<strong>Hybrid</strong> electric vehicles (HEVs), having multiple main<br />
energy sources, are an alternative to conventional<br />
vehicles. More <strong>and</strong> more importance is de<strong>di</strong>cated to<br />
research in this field of alternative vehicles. These<br />
energy sources are the conventional fuel tank <strong>and</strong> a<br />
battery, delivering both chemical <strong>and</strong> electrical energy.<br />
If a photovoltaic panel is also added, a <strong>Hybrid</strong> <strong>Solar</strong><br />
Vehicle (HSV) is obtained. HSVs can be seen as a<br />
transition from conventional vehicles to fully electric<br />
vehicles. The architecture of HSVs can be <strong>di</strong>fferent,<br />
depen<strong>di</strong>ng on the requirements imposed. Basic<br />
drivetrain structures for HSVs are: series, parallel,<br />
series/parallel <strong>and</strong> complex hybrids. Since the target of<br />
the research is optimization of fuel consumption in case<br />
of urban drive cycles, a series architecture was chosen<br />
for this study, this proving to be optimal in this case. A<br />
basic <strong>di</strong>agram of the series HSV is depicted in Figure 1.<br />
The first control strategy is based on dynamic<br />
programming (DP), which is actually used to obtain a<br />
global optimum for fuel consumption. The reference<br />
signal consists of several urban cycles.<br />
The result is an input sequence of battery nominal<br />
power values. Since DP is not a feasible solution for<br />
practical implementation due to its computational time,<br />
an alternative control strategy consists in Model<br />
Pre<strong>di</strong>ctive Control (MPC), implemented using the MPC<br />
Toolbox of the Matlab environment. Simulations were<br />
performed <strong>and</strong> presented in the paper for both<br />
strategies. To test <strong>and</strong> compare simulation results,<br />
st<strong>and</strong>ar<strong>di</strong>zed drive cycles had been defined in the<br />
literature, this paper focuses the simulations mainly on<br />
the so-called New European Driving Cycle (NEDC)<br />
<strong>and</strong> on the Federal Urban Driving Schedule (FUDS)<br />
which were presented in detail in (Bauer et al., 2002).<br />
Fig.1. Basic <strong>di</strong>agram of a series HSV<br />
2. FUEL CONSUMPTION MINIMIZATION USING<br />
DYNAMIC PROGRAMMING<br />
Optimal control of the series HSV was first achieved in<br />
this paper with dynamic programming. This is based on<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 11
Bellman’s principle which says that: “The parts of an<br />
optimal trajectory are all optimal trajectories”.<br />
This allows one to make calculations on a specific<br />
problem backward in time, with the assumption of<br />
optimal trajectory. The result of dynamic programming<br />
calculations is the optimal input sequence applicable to<br />
the system to achieve control goals. Dynamic<br />
programming gives the global optimal solution of the<br />
problem.<br />
Unfortunately this solution needs a priori knowledge of<br />
the reference signal <strong>and</strong> <strong>di</strong>sturbances on the entire time<br />
horizon considered in the calculations.<br />
This means that, the results of a dynamic programming<br />
solution can mainly be used just as a reference optimal<br />
solution to be compared with other control methods,<br />
such as MPC control in this paper.<br />
The other problem with dynamic programming is the<br />
time consuming calculations which prevent its<br />
application in real time solutions. For the used HSV<br />
model with NEDC drive cycle, the calculation of the<br />
optimal solution on a 1200 sec time horizon needed one<br />
hour on a PC with AMD 64 Athlon 3000+ processor<br />
<strong>and</strong> 1 GB DDR 400 RAM.<br />
In the following subsections the problem formulation,<br />
solution with dynamic programming <strong>and</strong> the results of<br />
this global optimal solution are <strong>di</strong>scussed.<br />
2.1 PROBLEM FORMULATION AND DYNAMIC<br />
PROGRAMMING SOLUTION<br />
The control goal of a HSV is the minimisation of fuel<br />
consumption over the whole time horizon considered in<br />
calculations. This can be achieved by proper switching<br />
(balancing) between the energy sources. In a HSV the<br />
electric motor’s (EM) power needs can be satisfied<br />
from the photovoltaic (PV) panel, battery <strong>and</strong> electric<br />
generator (EG). This means that one can optimize the<br />
use of this three energy sources. The electric power<br />
from PV panel depends on sun insolation <strong>and</strong> cell<br />
temperature (see Bauer et al. 2006). Unfortunately, one<br />
cannot control these parameters, so PV power cannot be<br />
a control variable, however it can improve the fuel<br />
economy of the vehicle.<br />
The system layout used for dynamic programming<br />
solution is depicted in figure 2.<br />
The notations used can also be seen in figure 2. The<br />
fuel consumption optimization can be achieved by the<br />
proper use of the EG <strong>and</strong> the battery, while satisfying<br />
drive power needs <strong>and</strong> sustaining battery state of charge<br />
(SOC), considering the whole time horizon. The power<br />
balance of the system is described by the following<br />
equation:<br />
P e Peg<br />
+ Pbn<br />
+ PPV<br />
= (1)<br />
On the right side, electric generator power <strong>and</strong><br />
Peg bn<br />
battery nominal power are the control variables.<br />
Pe electric motor power can be calculated from Pd<br />
drive power need, considering the characteristics of the<br />
EM. The controller can influence <strong>and</strong> P .<br />
Peg bn<br />
P<br />
Figure 2. System layout for dynamical programming<br />
However, if one gives , P is determined by<br />
Pbn eg<br />
equation 1. So the optimal solution of the control<br />
problem can be generated by the calculation of the Pbn<br />
sequence in time.<br />
In dynamic programming this can be achieved by a<br />
backward calculation from end of the drive cycle <strong>and</strong><br />
final value of the battery SOC. The start <strong>and</strong> end values<br />
of battery SOC must be the same (charge sustaining<br />
strategy).<br />
Of course, the drive cycle for the HSV must be a priori<br />
known. It the paper there were used the NEDC <strong>and</strong><br />
FUDS drive cycles, with given constant insolation <strong>and</strong><br />
temperature on PV panel.<br />
The charge sustainability gives limits on battery SOC in<br />
time. A <strong>di</strong>amond shaped limit set can be calculated for<br />
every vehicle <strong>and</strong> drive cycle as, it is presented in<br />
figure 3.<br />
Figure 3. Battery SOC bounds with NEDC drive cycle,<br />
1 kW/m 2 insolation <strong>and</strong> 25°C cell temperature<br />
The calculation are performed considering the possible<br />
SOC values at every time step, which can be achieved<br />
accor<strong>di</strong>ng to the constraint, SOC( 0)<br />
≡ SOC(<br />
end)<br />
, <strong>and</strong><br />
the minimal <strong>and</strong> maximal allowed SOC values. The<br />
minimal <strong>and</strong> maximal SOC values are 0.6 <strong>and</strong> 0.8<br />
respectively, from (Musardo et al. 2005). Both the<br />
upper <strong>and</strong> lower limits are described with three<br />
sections. These are the following:<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 12
1. Upper: the maximum possible SOC value<br />
which can be achieved from SOC(0) using<br />
maximum battery charge<br />
2. Upper: the maximum allowed SOC value<br />
3. Upper: the maximum SOC value from which<br />
SOC(end) can be achieved using maximum<br />
battery <strong>di</strong>scharge<br />
1. Lower: the minimum possible SOC value<br />
which can be achieved from SOC(0) using<br />
maximum battery <strong>di</strong>scharge<br />
2. Lower: the minimum allowed SOC value<br />
3. Lower: the minimum SOC value from which<br />
SOC(end) can be achieved using maximum<br />
battery charge<br />
Of course for these calculations the maximum <strong>and</strong><br />
minimum nominal battery charge powers have to be<br />
known for every time instant. The minimum power<br />
(<strong>di</strong>scharge power) is given by the limits of the battery.<br />
The maximum power (charge power) is given by the<br />
limits of the vehicle <strong>and</strong> can be calculated from (1):<br />
Pbn P<br />
max e − PPV<br />
− Pegmax<br />
= (2)<br />
In this form, reaches a negative value (if P <strong>and</strong><br />
Pbn eg<br />
PPV<br />
are assumed to be positive) which has to be<br />
considered in the battery calculations. In the presented<br />
example Pe<br />
is positive in EM driving mode <strong>and</strong><br />
negative in EM braking mode, which fits the<br />
calculations in (2).<br />
The calculated minimum <strong>and</strong> maximum powers for the<br />
case from figure 3 can be seen in figure 4.<br />
Figure 4. Maximum <strong>and</strong> minimum battery power, with<br />
drive power need (NEDC drive cycle, 1 kW/m 2<br />
insolation <strong>and</strong> 25°C cell temperature)<br />
In figure 4 it can be seen that the maximum charge<br />
power (negative accor<strong>di</strong>ng to (2)) has a minimum point<br />
(in absolute value) where the drive power need is<br />
maximal.<br />
After calculating the possible battery SOC limits, the<br />
solution can be achieved with dynamic programming.<br />
This starts from SOC(end) <strong>and</strong> Pd (end)<br />
stepping<br />
backward in time. This way in every time step the<br />
optimal fuel use until the end of drive cycle is<br />
calculated. Finally, the minimum fuel path is selected as<br />
an optimal solution.<br />
In every step k the possible battery SOC range has to be<br />
considered <strong>and</strong> compared with the next range (step<br />
k+1) calculated in the previous step. For every SOC<br />
value in range k all possible SOC trajectories to range<br />
k+1 have to be calculated (limited with maximum<br />
battery charge <strong>and</strong> <strong>di</strong>scharge). This is illustrated<br />
schematically in figure 5.<br />
Figure 5. Sketch of dynamical programming solution<br />
After determining the possible charge <strong>and</strong> <strong>di</strong>scharge<br />
range (considering the limits), it can calculated the ICE<br />
fuel consumption for every trajectory from step k to<br />
k+1. Ad<strong>di</strong>ng these fuel consumptions to every total fuel<br />
consumption from step k+1 to end, there result the<br />
possible total fuel consumptions from k to end starting<br />
from SOC(k). The minimum of the total fuel<br />
consumptions give the global optimal trajectory from<br />
SOC(k) to SOC(end). In step k these are calculated <strong>and</strong><br />
stored for every possible SOC(k) values.<br />
After completing this procedure, in SOC(0) step, the<br />
global optimal total fuel consumption results. The<br />
optimal SOC trajectory can be determined following the<br />
minimum fuel path from SOC(0) to SOC(end). This<br />
results in the optimal Pbn<br />
sequence in time.<br />
This optimal input sequence can than be applied to the<br />
Simulink model of the vehicle. Test results are given in<br />
the following subsection.<br />
2.2 CALCULATION AND TEST RESULTS FROM<br />
DYNAMIC PROGRAMMING<br />
Calculations were performed for NEDC <strong>and</strong> FUDS<br />
drive cycles, considering the whole range of sun<br />
insolation on 25°C cell temperature. Reference results,<br />
without controller (but with battery charge with<br />
regenerative braking) were generated in (Bauer et al.<br />
2006). They are summarized in table 1:<br />
λ [kW/m 2 ] 1 0.8 0.6 0.4 0.2 0<br />
SOC 0.7192 0.7189 0.7186 0.7183 0.7181 0.7178<br />
total fuel [g] 913.7265 916.015 918.1686 920.4583 922.613 924.768<br />
NEDC<br />
λ [kW/m 2 ] 1 0.8 0.6 0.4 0.2 0<br />
SOC 0.7125 0.7122 0.7119 0.7116 0.7113 0.711<br />
total fuel [g] 499.696 502.8127 505.7325 509.5911 513.0373 515.9575<br />
FUDS<br />
Table 1. Reference results without controller<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 13
The dynamic programming gave less total fuel<br />
consumption in every case.<br />
Optimal SOC trajectory, fuel consumption <strong>and</strong> Pbn<br />
sequences are presented in figure 6, 7 <strong>and</strong> 8 for NEDC<br />
2<br />
drive cycle, 1 kW/m insolation <strong>and</strong> 25°C cell<br />
temperature. The SOC trajectory lies between the limits<br />
in every time step, moreover, it is near the desired value<br />
(0.7) during the entire time range. In fuel consumption<br />
(figure 7) horizontal sections mean that the ICE was<br />
turned off <strong>and</strong> no fuel consumption occurred during that<br />
time range. This is the case of regenerative braking or<br />
low power need satisfied from PV power. In Pbn<br />
sequence regenerative braking is strongly used to<br />
improve fuel economy.<br />
Figure 6. SOC trajectory from NEDC drive cycle<br />
Figure 7. Fuel consumption from NEDC drive cycle<br />
Figure 8. Optimal Pbn<br />
sequence from NEDC drive<br />
cycle<br />
Results from dynamic programming are summarized in<br />
table 2, while results from MATLAB Simulink vehicle<br />
model simulations with optimal Pbn<br />
sequence are<br />
summarized in table 3 (about the vehicle modelling,<br />
details can be found in (Bauer et al. 2006)).<br />
λ [kW/m 2 ] 1 0.8 0.6 0.4 0.2 0<br />
total fuel [g] 811.6438 814.3697 817.516 820.4546 822.6674 835.5047<br />
fuel spare [%] 11.172 11.096 10.96 10.865 10.8329 9.6525<br />
NEDC<br />
λ [kW/m 2 ] 1 0.8 0.6 0.4 0.2 0<br />
total fuel [g] 369.0273 374.4629 380.9043 388.4347 393.5668 396.6046<br />
fuel spare [%] 26.15 25.526 24.68 23.77 23.287 23.13<br />
FUDS<br />
Table 2. Results from dynamic programming<br />
λ [kW/m 2 ] 1 0.8 0.6 0.4 0.2 0<br />
SOC 0.7004 0.7005 0.7005 0.7005 0.7004 0.7004<br />
total fuel [g] 855.8369 585.2858 858.8072 860.8495 863.4588 872.5427<br />
fuel spare [%] 6.336 6.302 6.4652 6.47 6.4116 5.647<br />
NEDC<br />
λ [kW/m 2 ] 1 0.8 0.6 0.4 0.2 0<br />
SOC 0.7009 0.7008 0.7009 0.701 0.7009 0.7008<br />
total fuel [g] 421.7183 426.8949 434.7972 440.241 442.2611 444.2961<br />
fuel spare [%] 15.605 15.098 14.026 13.609 13.796 13.889<br />
FUDS<br />
Table 3. Results from simulations with optimal<br />
input sequence<br />
Pbn<br />
As it is presented in table 2, DP results are almost the<br />
same for <strong>di</strong>fferent insolation values, calculating with<br />
the same drive cycle. In the case of NEDC, the fuel<br />
spare ranges from 9.7 to 11.2 %, while in the case of<br />
FUDS it ranges from 23.13 to 26.15 %. This is mainly<br />
because NEDC needs higher drive power, which means<br />
more intensive battery use <strong>and</strong> constrained alternator<br />
usability for battery charge. Battery SOC is originally<br />
sustained by DP calculations.<br />
Table 3 shows that in the case of system model<br />
simulation with optimal Pbn<br />
input sequence lower fuel<br />
spare values can be achieved. This is due to continuous<br />
dynamics of the battery, in spite of moving between<br />
<strong>di</strong>screte battery charge level values as it was in the DP<br />
solution. However, overall charge sustainability<br />
requirement is satisfied in each case (see Table 3, SOC<br />
values).<br />
Finally it is worth noting that, these results were<br />
calculated without limitation in changes of battery, EG<br />
<strong>and</strong> ICE power. So, sudden changes were allowed, as<br />
can be seen in figure 8. In real applications, of course,<br />
the limitation of battery power, EG power <strong>and</strong> ICE<br />
power derivatives have to be considered. This is the<br />
objective of future research <strong>and</strong> will decrease the fuel<br />
economy of the vehicle, but it is required for control<br />
strategy feasibility.<br />
3. MODEL PREDICTIVE CONTROL FOR FUEL<br />
CONSUMPTION MINIMIZATION<br />
The second control strategy that was applied for the<br />
series HSV architecture is Model Pre<strong>di</strong>ctive Control<br />
(MPC), as used also for a hybrid vehicle in (Back et al.,<br />
2002). MPC is an advanced control strategy which had<br />
spread significantly during the past years in industry as<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 14
well, due to its increasing popularity (Camacho <strong>and</strong><br />
Bordons, 1999). The main advantages of MPC is that<br />
the basic formulation is extended to MIMO plants with<br />
almost no mo<strong>di</strong>fication, on the other h<strong>and</strong> the basic<br />
concept of MPC is relatively easy to underst<strong>and</strong>, <strong>and</strong> it<br />
is a powerful tool to cope with constraints effectively<br />
(Maciejowski, 2002). Without getting into a detailed<br />
presentation of MPC algorithms, the basic “elements”<br />
that build the problem formulation are the following:<br />
• Cost function that penalizes the deviations of the<br />
pre<strong>di</strong>cted outputs from the reference trajectories;<br />
• Internal model of the plant;<br />
• Reference trajectory for the desired closed-loop<br />
trajectory;<br />
• Possibility of defining constraints;<br />
• On-line optimization to determine the future<br />
control strategy;<br />
• Rece<strong>di</strong>ng horizon principle.<br />
For design <strong>and</strong> simulation of the fuel consumption<br />
minimization for a series HSV, the MPC Toolbox of<br />
Matlab is used. In this sense, the problem formulation<br />
follows the steps <strong>and</strong> form required by this design tool,<br />
based on the above presented elements.<br />
The first element to be defined is the plant model that is<br />
used in the pre<strong>di</strong>ctive controller. This model is<br />
presented in detail in (Bauer et al., 2006), based on a<br />
detailed presentation of the components <strong>and</strong> their<br />
models.<br />
As it can be noted from (Bauer et al., 2006), the model<br />
is non-linear, so in order to apply the MPC tools a<br />
linearization is needed prior to it. This is achieved<br />
through the Matlab function linmod2, which creates a<br />
linear model from the non-linear system using an<br />
advanced method. The advantage is that the state<br />
variables of the system remain the original ones, so the<br />
physical meaning of the chosen state variables is kept.<br />
Accor<strong>di</strong>ng to this, the states, inputs <strong>and</strong> outputs of the<br />
linearized plant are:<br />
� State variables: - x1: ICE power state,<br />
- x2: SOC,<br />
� Inputs: - u1: ICE power,<br />
- x3: EM power state;<br />
- u2: Battery nominal power;<br />
� Controlled outputs: - o1: Drive power,<br />
- o2: SOC,<br />
- o3: Fuel rate;<br />
� Measured <strong>di</strong>sturbance input: - dm: PV panel<br />
power.<br />
The PV power is considered as a measured <strong>di</strong>sturbance<br />
(since it depends on the actual insolation which is an<br />
external factor that cannot be influenced) <strong>and</strong> treated as<br />
such, both in the modelling phase <strong>and</strong> in the controller<br />
design phase (Kulcsar <strong>and</strong> Bokor, 2006), (Maciejowski,<br />
2002).<br />
For a SISO case, the basic idea for designing an<br />
application for the MPC Toolbox is depicted in figure<br />
9, based on (Bemporad et.al., 2006).<br />
MPC<br />
Controller<br />
Plant<br />
Figure 9. Bloc <strong>di</strong>agram of a SISO MPC<br />
Toolbox Application<br />
The numerical values for the linearized <strong>and</strong> sampled<br />
state-space model are (sampling time of Ts=0.001 sec.<br />
was chosen).<br />
⎡x<br />
1(<br />
k + 1)<br />
⎤ ⎡0.<br />
3679<br />
⎢ ⎥<br />
=<br />
⎢<br />
⎢<br />
x 2 ( k + 1)<br />
⎥ ⎢<br />
0<br />
⎢⎣<br />
x ( k + 1)<br />
⎥⎦<br />
⎢<br />
3 ⎣ 0<br />
⎡ 3.<br />
78⋅10<br />
⎢<br />
+ ⎢ 0<br />
⎢<br />
⎣2.<br />
638⋅10<br />
−6<br />
−7<br />
−4<br />
⎡6. 321⋅10<br />
⎤<br />
⎢ ⎥<br />
+ ⎢ 0 ⎥d<br />
⎢ ⎥<br />
⎣ 0 ⎦<br />
⎡y1<br />
( k)<br />
⎤ ⎡800<br />
0<br />
⎢ ⎥<br />
=<br />
⎢<br />
⎢<br />
y 2 ( k)<br />
⎥ ⎢<br />
0 1<br />
⎢⎣<br />
y ( k)<br />
⎥⎦<br />
⎢<br />
3 ⎣ 0 0<br />
6.<br />
321⋅10<br />
−<br />
−1.<br />
517 ⋅10<br />
m<br />
( k)<br />
0<br />
1<br />
0<br />
0<br />
0 ⎤⎡x<br />
1(<br />
k)<br />
⎤<br />
0<br />
⎥⎢<br />
⎥<br />
⎥⎢<br />
x 2 ( k)<br />
⎥<br />
+<br />
0.<br />
9048⎥⎦<br />
⎢⎣<br />
x ( k)<br />
⎥ 3 ⎦<br />
−4<br />
11<br />
0 ⎤⎡<br />
x1(<br />
k)<br />
⎤<br />
0<br />
⎥⎢<br />
⎥<br />
⎥⎢<br />
x 2 ( k)<br />
⎥<br />
100⎥⎦<br />
⎢⎣<br />
x ( k)<br />
+ ⎥ 3 ⎦<br />
⎤<br />
⎥⎡u<br />
1(<br />
k)<br />
⎤<br />
⎥⎢<br />
⎥ +<br />
⎥⎣u<br />
2 ( k)<br />
⎦<br />
⎦<br />
( 3)<br />
The system is both observable <strong>and</strong> controllable, so<br />
MPC can be applied without problems.<br />
The acting constraints that are defined for the problem<br />
are the following:<br />
⎧0<br />
≤ u1<br />
≤ 93000<br />
⎪<br />
⎪<br />
− 26000 ≤ u 2 ≤14000<br />
(4)<br />
⎨−<br />
40000 ≤ y1<br />
≤ 58000<br />
⎪0.<br />
6 ≤ y 2 ≤ 0.<br />
8<br />
⎪<br />
⎪⎩<br />
0 ≤ y 3 ≤ 7.<br />
3<br />
The next step is the definition of the cost function that<br />
is used for the optimization. The aim is the fuel<br />
consumption minimization for the series HSV. A<br />
quadratic cost function is assumed that has the<br />
following form:<br />
J ( k)<br />
=<br />
N2<br />
∑<br />
2<br />
yˆ<br />
( k + i k)<br />
− r(<br />
k + i k)<br />
Q(<br />
i)<br />
+<br />
i=<br />
N1<br />
(5)<br />
Nu<br />
∑<br />
i=<br />
0<br />
∆uˆ<br />
( k + i k)<br />
2<br />
R(<br />
i)<br />
Where y ˆ( k + i k)<br />
are the pre<strong>di</strong>ctions, at time k, of the<br />
output y, r ( k + i k)<br />
is the reference trajectory vector,<br />
∆u<br />
ˆ( k + i k)<br />
are the changes of the future input vector<br />
(this term is necessary to ensure the reference tracking<br />
behaviour).<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 15
The tuning parameters of the cost function are as<br />
follows:<br />
• Pre<strong>di</strong>ction horizon: = 1,<br />
N = 10<br />
• Control horizon: Nu=4<br />
N1 2<br />
• Penalties:<br />
−4<br />
⎡10<br />
⎢<br />
Q = ⎢ 0<br />
⎢<br />
⎣ 0<br />
0<br />
1000<br />
0<br />
0 ⎤<br />
⎥<br />
0 ⎥,<br />
0.<br />
01⎥<br />
⎦<br />
−15<br />
⎡10<br />
R = ⎢<br />
⎣ 0<br />
0 ⎤<br />
−15<br />
⎥<br />
10 ⎦<br />
The tuning parameters can be mo<strong>di</strong>fied to obtain<br />
<strong>di</strong>fferent performances.<br />
After defining the required parameters, the problem<br />
setup can be transposed into the following Matlab<br />
design tool (GUI of the MPC Toolbox) (figure 10).<br />
With its help, the final adjustments <strong>and</strong> also parameter<br />
mo<strong>di</strong>fications for new setups can be easily performed.<br />
Figure 10. GUI setup for the given problem<br />
The first simulation was the application of the NEDC<br />
drive cycle, transposed into required reference of drive<br />
power for r1 which is presented in figure 4. Also, for the<br />
SOC the constant reference of r2=0.7 was held, the<br />
third reference was r3=0 (for fuel rate).<br />
The simulation results are depicted in figures 11<br />
(reference tracking), figure 12 (SOC <strong>and</strong> total fuel) <strong>and</strong><br />
figure 13 (control signals ICE power <strong>and</strong> battery<br />
nominal power).<br />
It can be seen that the reference tracking is ensured by<br />
the pre<strong>di</strong>ctive controller. The fuel consumption is<br />
between the global optimum value <strong>and</strong> the value<br />
calculated without controller (see table 1.). The SOC<br />
ensures a lower final value compared to the DP. This<br />
can be taken into account at a later global evaluation.<br />
Secondly, a <strong>di</strong>fferent st<strong>and</strong>ard drive cycle is applied,<br />
namely the FUDS, presented in figure 14, together with<br />
the system output. The tuning parameters of the<br />
controller are the same as in the NEDC case.<br />
m f [g]<br />
P d [W]<br />
SOC<br />
x<br />
PD<br />
104<br />
6<br />
5<br />
4<br />
3<br />
2<br />
1<br />
0<br />
-1<br />
-2<br />
-3<br />
reference signal tracking<br />
Output<br />
Reference<br />
-4<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
0.72<br />
0.7<br />
0.68<br />
0.66<br />
Figure 11. NEDC reference tracking<br />
SOC<br />
0.64<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
P ICE<br />
P bn<br />
1000<br />
800<br />
600<br />
400<br />
200<br />
Total fuel<br />
0<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
6<br />
4<br />
2<br />
0<br />
Figure 12. NEDC SOC <strong>and</strong> total fuel consumption<br />
x<br />
ICE<br />
104<br />
8<br />
power<br />
-2<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
x<br />
Battery<br />
104<br />
2<br />
1<br />
0<br />
-1<br />
-2<br />
nominal power<br />
-3<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
Figure 13. ICE power <strong>and</strong> nominal battery power<br />
The same signals are plotted as in the NEDC case, for<br />
comparison, namely the SOC <strong>and</strong> total fuel<br />
consumption (figure 15) <strong>and</strong> ICE power plus battery<br />
nominal power (figure 16).<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 16
P d [W]<br />
SOC<br />
m f [g]<br />
P ICE<br />
P bn<br />
x<br />
PD<br />
104<br />
1.5<br />
1<br />
0.5<br />
0<br />
-0.5<br />
reference signal tracking<br />
Output<br />
Reference<br />
-1<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
0.71<br />
0.7<br />
0.69<br />
Figure 14. FUDS reference tracking<br />
SOC<br />
0.68<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
600<br />
400<br />
200<br />
0<br />
Total fuel<br />
-200<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
1.5<br />
1<br />
0.5<br />
x ICE 104<br />
2<br />
Figure 15. FUDS SOC <strong>and</strong> total fuel consumption<br />
power<br />
0<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
x<br />
Battery<br />
104<br />
2<br />
1<br />
0<br />
-1<br />
nominal power<br />
-2<br />
0 200 400 600<br />
Time [sec]<br />
800 1000 1200<br />
Figure 16. FUDS ICE power <strong>and</strong> battery nominal<br />
power<br />
It can be remarked that for the case when the FUDS<br />
drive cycle is used, the reference tracking is ensured<br />
acceptably well by the pre<strong>di</strong>ctive controller. The fuel<br />
consumption is between the global optimum value <strong>and</strong><br />
the value calculated without controller (see table 1.).<br />
The SOC ensures a lower final value compared to the<br />
DP.<br />
7. CONCLUSIONS<br />
The paper presents two solutions for fuel consumption<br />
optimization of a series <strong>Hybrid</strong> <strong>Solar</strong> Vehicle (HSV).<br />
HSVs, having multiple main energy sources, are an<br />
alternative to conventional vehicles.<br />
Based on a brief description of the model of a series<br />
HSV, two control strategies are presented for fuel<br />
consumption optimization.<br />
The first control strategy is dynamic programming (DP)<br />
which is used to obtain a global optimum for fuel<br />
consumption. This is not an on-line solution, since it<br />
assumes that the future reference is entirely known. In<br />
the paper a DP solution was given, showing that the<br />
energy management concept is working for pre-defined<br />
drive-cycles.<br />
The second control algorithm is Model Pre<strong>di</strong>ctive<br />
Control, implemented using the MPC Toolbox of<br />
Matlab. Simulations were performed for two drive<br />
cycles, namely for the New European Drive Cycle <strong>and</strong><br />
for the Federal Urban Drive Schedule. In both cases the<br />
results are satisfactory, both concerning reference<br />
tracking <strong>and</strong> fuel consumption minimization. The fuel<br />
consumption lies between the global optimum values<br />
(calculated with DP) <strong>and</strong> values without controller. The<br />
results are very promising, still further research is<br />
needed to improve the methodology.<br />
The test simulations are performed for both strategies<br />
using Matlab/Simulink environment .<br />
ACKNOWLEDGEMENTS<br />
The authors gratefully acknowledge the contribution of<br />
Hungarian National Science foundation (OTKA<br />
N:K060767). This work was partially supported by the<br />
Hungarian National Office for Research <strong>and</strong><br />
Technology through the project "Advanced <strong>Vehicles</strong><br />
<strong>and</strong> Vehicle Control Knowledge Center" (no: OMFB -<br />
01418/2004).<br />
REFERENCES<br />
I.Arsie, M.Graziosi, C.Pianese, G.Rizzo, M. Sorrentino<br />
(2004). Optimization of Supervisory Control<br />
Strategy for Parallel <strong>Hybrid</strong> Vehicle with<br />
Provisional Load Estimate, AVEC ’04 (Department<br />
of Mechanical Engineering – University of <strong>Salerno</strong>).<br />
M.Back, M. Simons, F. Kirschaum, V. Krebs (2002).<br />
Pre<strong>di</strong>ctive Control of Drivetrains, IFAC 15 th<br />
Triennial World Congress, Barcelona, Spain.<br />
P.Bauer, Zs. Preitl, T. Peter, P. Gaspar, Z. Szabo, J.<br />
Bokor (2006). Control oriented modelling of a<br />
series hybrid solar vehicle, Workshop on <strong>Hybrid</strong><br />
<strong>Solar</strong> <strong>Vehicles</strong>, November 6, 2006, University of<br />
<strong>Salerno</strong>, Italy.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 17
A. Bemporad, M. Morari, N.L. Ricker (2006). Model<br />
Pre<strong>di</strong>ctive Control Toolbox for Use with Matlab,<br />
Users’ guide, Version 2, The Mathworks Inc.<br />
E.F. Camacho, C. Bordons (1999). Model Pre<strong>di</strong>ctive<br />
Control, Springer Verlag London Ltd.<br />
G.Gutmann (1999). <strong>Hybrid</strong> electric vehicles <strong>and</strong><br />
electrochemical storage systems – a technology<br />
push – pull couple, Journal of Power Sources, Vol.<br />
84, pp. 275-279.<br />
M.W.T. Koot, J.T.B.A. Kessels, A.G. de Jager,<br />
W.P.M.H. Heemels, P.P.J. van den Bosch, M.<br />
Steinbuch (2005). Energy Management Strategies<br />
for Vehicular Electric Power Systems, IEEE Trans.<br />
on Vehicular Technology, 54(3), 771-782,.<br />
B. Kulcsar, J. Bokor (2006). Measured Disturbance<br />
Estimation for Model Pre<strong>di</strong>ctive Controller,<br />
Me<strong>di</strong>terranean Journal of Measurement <strong>and</strong><br />
Control, Vol 2., No 3, July 2006.<br />
S.E. Lyshevski (2000). Energy conversion <strong>and</strong> optimal<br />
energy management in <strong>di</strong>esel-electric drivetrains of<br />
hybrid-electric vehicles, Energy Conversion &<br />
Management, Vol. 41, pp. 13-24,.<br />
J.M. Maciejowski (2002). Pre<strong>di</strong>ctive Control with<br />
Constraints, Pearson Education Ltd.<br />
G.Maggetto, J. van Mierlo (2001). Electric vehicles,<br />
hybrid electric vehicles <strong>and</strong> fuel cell electric<br />
vehicles: state of the art <strong>and</strong> perspectives, Ann.<br />
Chim. Sci. Mat, Vol. 26(4), pp. 9-26.<br />
C. Musardo, G. Rizzoni, Y.Guezennec, B. Staccia<br />
(2005). A - ECMS: An Adaptive Algorithm for<br />
<strong>Hybrid</strong> Electric Vehicle Energy Management,<br />
European Journal of Control, 11 (4-5), pp. 509-524.<br />
S. Piller, M. Perrin, A. Jossen (2001). Methods for<br />
state-of-charge determination <strong>and</strong> their applications,<br />
Journal of Power Sources, Vol. 96, pp. 113-120.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 18
CONTROL ORIENTED MODELLING OF A SERIES HYBRID SOLAR VEHICLE<br />
P. Bauer*, Zs. Preitl*, T. Péter*,P. Gáspár**,Z. Szabó**, J. Bokor**<br />
* Budapest University of Technology <strong>and</strong> Economics, Dept. Of Transport Automation,<br />
H-1111 Budapest, Bertalan L.u. 2., Hungary<br />
Email: bauer.peter@mail.bme.hu, preitl@sch.bme.hu, bokor@sztaki.hu<br />
** Computer <strong>and</strong> Automation Research Institute,<br />
H-1518 Budapest, Kende u. 13-17, Hungary<br />
Abstract: Nowadays more <strong>and</strong> more importance is de<strong>di</strong>cated to research in the field of<br />
alternative vehicles. An option to conventional vehicles, having usually as energy source<br />
a fuel tank with gasoline, consists in the so called hybrid electric vehicles (HEVs) which<br />
have multiple main energy sources. These energy sources are the conventional fuel tank<br />
<strong>and</strong> a battery, delivering both chemical <strong>and</strong> electrical energy. This can be completed with<br />
a photovoltaic (PV) panel resulting in a hybrid solar vehicle (HSV). HEVs <strong>and</strong> HSVs can<br />
be seen as a transition from conventional vehicles to fully electric ones. The paper<br />
presents a study on modelling a series HSV. The model can be used for the development<br />
of optimal control strategies which minimize the vehicle’s fuel consumption. After<br />
modelling all of the components of the HSV, two simulation structures were built in<br />
MATLAB Simulink. The first for basic simulations without control, the second for<br />
controller design for example with MPC Toolbox. The basic model is mainly a backward<br />
calculation scheme <strong>and</strong> provides reference solutions which can be compared with the<br />
controlled system behaviour. The control oriented model is a forward calculation scheme<br />
with given states, inputs <strong>and</strong> outputs. Linear models can be generated from it, were all<br />
states are controllable <strong>and</strong> observable.<br />
Keywords: hybrid solar vehicles (HSVs), component models, backward <strong>and</strong> forward<br />
calculations<br />
1. INTRODUCTION<br />
The paper presents a study on modelling a series HSV.<br />
Series HSVs are optimal solutions for urban traffic<br />
applications where the vehicle starts <strong>and</strong> stops<br />
frequently during a drive cycle. So regenerative braking<br />
can be often used, which substantially improves the fuel<br />
economy of the vehicle. However, a series structure<br />
applies fully electric driving, where instantaneous<br />
large tractive forces provide good acceleration for the<br />
vehicle. The overall structure of series architecture is<br />
presented in figure 1.<br />
The vehicle model can be used for the development of<br />
optimal control strategies which minimize the vehicle’s<br />
fuel consumption. Finally, two types of models were<br />
generated.<br />
The first model, which is meant for basic calculations,<br />
provides reference data about the vehicle without<br />
controller. In this model, one can consider that<br />
regenerative braking only charges the battery, other<br />
control actions were not applied. The simulation<br />
scheme is mainly a backward calculation which<br />
determines the inputs from the required system outputs.<br />
It can also be used for control action design with<br />
dynamical programming.<br />
Figure 1. Series hybrid architecture<br />
The second model that can be used for controller<br />
design, uses forward calculation scheme with given<br />
states, inputs <strong>and</strong> outputs. Controllers can be designed<br />
using this scheme, for example using the MPC Toolbox<br />
of Matlab.<br />
In the second section the specifications of all<br />
components of the series hybrid driveline are given.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 19
The third section deals with MATLAB Simulink model<br />
construction <strong>and</strong> basic vehicle simulations. So reference<br />
data was generated about the HSV. Finally the<br />
conclusions end this paper.<br />
2. COMPONENT MODELLING IN A SERIES<br />
HYBRID ARCHITECTURE<br />
The architecture of a series hybrid vehicle can be seen<br />
in figure 1. First, the basic dynamics of the vehicle have<br />
to be considered, using <strong>di</strong>fferent drive cycles. This way<br />
the extreme values of required drive power, torque <strong>and</strong><br />
angular velocity can be calculated.<br />
After these calculations, the proper driveline elements<br />
can be chosen which fit the requirements. These<br />
elements are the following:<br />
The main part is the electric motor (EM) which drives<br />
the wheels or works as a generator during regenerative<br />
braking. The electrical energy for the EM is delivered<br />
by the electric generator (EG), the photovoltaic (PV)<br />
panel <strong>and</strong> battery. The electric generator is in rigid<br />
connection with the internal combustion engine (ICE).<br />
These two components have to be considered as an<br />
integral part of the vehicle, so power range, working<br />
points <strong>and</strong> efficiencies must be fitted. The internal<br />
combustion engine can be a <strong>di</strong>esel or a gasoline engine.<br />
The EM considered in such applications is usually a<br />
brushless DC motor which can be used both in motor<br />
<strong>and</strong> generator modes.<br />
PV panels can be used mainly during parking of the<br />
vehicle, but on open area, they are useful supplements<br />
for the electric power sources (EG <strong>and</strong> Battery) in<br />
driving too.<br />
The vehicle management unit (VMU) is used for<br />
control <strong>and</strong> coor<strong>di</strong>nation of the components. When<br />
designing the control strategies, one must consider the<br />
properties of all the components <strong>and</strong> the goals of the<br />
control application. Usually the main goals are<br />
minimum fuel consumption during a trip <strong>and</strong> battery<br />
charge sustaining.<br />
In the following subsections the modelling of each is<br />
component is presented in detail.<br />
2.1 VEHICLE USED FOR HSV DEVELOPMENT<br />
As a base vehicle, we selected the Porter glass van (see<br />
figure 2) used at the University of <strong>Salerno</strong>. Few<br />
technical data about the vehicle can be found in (Porter<br />
2005-2006), but it is not enough even for basic<br />
dynamical calculations. So, one has to search for data<br />
about a similar van. This was the Subaru Libero mini<br />
van (Subaru 2006). Using the data about both vehicles,<br />
the parameters of the vehicle model are following:<br />
o m=1400kg vehicle mass<br />
o Ad=2.724 m 2 frontal area<br />
o Cd=0.6 air drag coefficient<br />
o Cr=0.015 rolling resistance coefficient<br />
o ρ=1.225 kg/m 3 air density<br />
o wr=0.3m wheel ra<strong>di</strong>us<br />
o fr=4 final drive ratio<br />
o Battery voltage: 84V 6 x 14V cells<br />
o Battery capacity: 180Ah<br />
Figure 2. Porter glass van (Porter 2005-2006, Micro-<br />
Vett SPA)<br />
For component selection, one has to calculate the<br />
power, torque <strong>and</strong> angular velocity requirements for the<br />
EM. This can be achieved using <strong>di</strong>fferent drive cycles<br />
<strong>and</strong> the well known basic dynamical relations in the<br />
motion of vehicle. These relations are as follows:<br />
f<br />
ω()<br />
t = r v() t<br />
w<br />
r<br />
wr<br />
Md() t = Fd() t<br />
fr<br />
1 2<br />
Fd() t = m⋅ v&+ ρv<br />
() t ⋅Ad ⋅ Cd + m⋅g⋅Cr 2<br />
(1)<br />
Where ω is the angular velocity <strong>and</strong> Md is the torque<br />
required from the EM. The velocity v(t) is given in the<br />
specified drive cycles (for example figure 14, 15) <strong>and</strong><br />
the acceleration ( vt &()<br />
) can be simply calculated from<br />
it. So the required values for a given vehicle <strong>and</strong> drive<br />
cycle can be estimated. The considered drive cycles are:<br />
ECE_15, NEDC (New European Driving Cycle), FUDS<br />
(Federal Urban Driving Schedule), FHDS (Federal<br />
Highway Driving Schedule). The calculated maximal<br />
power, torque <strong>and</strong> angular velocity requirements are<br />
summarized in table 1.<br />
Drive cycle P max [W] M dmax [Nm] ω max [rad/s]<br />
ECE_15 15120 118.2 185.2<br />
NEDC 57089 234.52 444.45<br />
FUDS 10334 179.25 209.7<br />
FHDS 35075 162.3 357.375<br />
Table 1. Power, torque <strong>and</strong> angular velocity<br />
requirements<br />
As it can be observed, the EM must be able to deliver at<br />
least 57089W maximum power. So, the choice of an<br />
EM with 58 kW maximum mechanical power is<br />
suitable for this vehicle. Of course the ICE <strong>and</strong> EG<br />
must be fitted for this EM. This aspect will be <strong>di</strong>scussed<br />
later in subsections dealing with ICE <strong>and</strong> EG.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 20
2.2 ELECTRIC MOTOR (EM)<br />
Usually an attractive alternative for electric vehicles<br />
<strong>and</strong> HEV driving systems are Brushless DC machines<br />
(BLDC-m) (Crowder, 1998), (Ehsani et al, 2001). They<br />
can function both in motor <strong>and</strong> generator regimes. As a<br />
remark to the BLDCs, it can be mentioned that the<br />
BLDC is in fact the combination of a permanently<br />
excited synchronous motor <strong>and</strong> a frequency inverter,<br />
where the inverter „replaces” the converter of a<br />
classical DC motor (Rizzoni, 1993), (Filippa et al,<br />
2004). From here results also the name Brushless DC<br />
motor. BLDCs with inverter are mainly used in high<br />
performance electric drives with variable speed, where<br />
these values largely outrun the nominal rotation<br />
velocity.<br />
The BLDC-m is with “rare earth” magnetic materials<br />
(Samarium-Cobalt (Sm-Co) or other materials), which<br />
combine high flux-density with very large coercive<br />
force. The BLDC-m has its own electro-mechanical<br />
characteristics, it can not be used without a de<strong>di</strong>cated<br />
power supply unit <strong>and</strong> control system, consisting in: the<br />
power electronics unit: DC-AC or DC-AC - AC-DC<br />
(inverter), the comm<strong>and</strong> <strong>and</strong> the control unit (<strong>di</strong>gital<br />
control unit), the BLDC-m servo-unit (Bay et al., 1996).<br />
A suitable solution consists in using DC-AC (AC-DC –<br />
for regenerative braking) inverter supply which ensures<br />
the torque control with injected current (PWM<br />
modulated control).<br />
The four-quadrant operation mode for the BLDCmachine<br />
with control block is presented below in figure<br />
3, based on (Tsai, 2002).<br />
Figure 3. Operation modes for a BLDC-m<br />
In the paper the aspects regar<strong>di</strong>ng BLDC-m modelling<br />
refer to a qualitative modelling (machine plus power<br />
electronics structure) (Tsai, 2002), details regar<strong>di</strong>ng the<br />
pure machine are not presented. The qualitative<br />
modelling is achieved through the presentation of static<br />
characteristics, with two possibilities:<br />
• Steady-state torque-speed curves,<br />
ω = f ( M;<br />
U − parameter)<br />
. The characteristics are<br />
based on relation:<br />
M = Kt<br />
I − I ) <strong>and</strong> I ≈ 0.<br />
1⋅<br />
I =><br />
( 0<br />
1 1.<br />
1<br />
= 0.<br />
9K<br />
I => I = M = M (2)<br />
0.<br />
9 ⋅ K K<br />
M t<br />
t<br />
0<br />
t<br />
n<br />
ω =<br />
1 1,<br />
1<br />
[ U − Rm<br />
M ]<br />
Ke Kt<br />
Where M is the torque, I is current, U is voltage, Kt, Ke,<br />
are the electromechanical <strong>and</strong> the electromagnetic<br />
constants of the machine (their values are numerically<br />
close).<br />
• Steady-state speed-torque curves<br />
M = f ( ω ; U − parameter)<br />
; they are obtained by<br />
inversing relation:<br />
Kt<br />
M = [ U − Keω]<br />
1.<br />
1Rm<br />
(3)<br />
The characteristic steady-state curves for this latter case<br />
are presented in figure 4 (in normalised values). The<br />
<strong>di</strong>agram is presented in normalized values of the torque<br />
<strong>and</strong> speed, for the first quadrant accor<strong>di</strong>ng to figure 3.<br />
nn is the nominal resolution, in Pel=Pmax =constant<br />
regime.<br />
Figure 4. Torque-speed characteristics in normalized<br />
values<br />
Torque (Nm)<br />
200<br />
150<br />
100<br />
50<br />
0<br />
-50<br />
-100<br />
-150<br />
-200<br />
Brushless DC motor drive <strong>and</strong> brake characteristics<br />
0 500 1000 1500 2000 2500 3000 3500 4000 4500<br />
Speed (rpm)<br />
Figure 5. Speed-torque characteristics for quadrants I<br />
<strong>and</strong> II.<br />
For the given numerical data (nn=2300 rpm nominal<br />
RPM, Pn=58kW nominal mechanical power, Un=84V<br />
armature voltage, η = 0. 8 efficiency factor) the speedtorque<br />
characteristics are given in figure 5, for <strong>di</strong>fferent<br />
values of the armature voltage. It must be mentioned<br />
that the axes is figure 4 <strong>and</strong> figure 5 are inverted to the<br />
axes of figure 3. The maximum torque is obtained at<br />
the nominal armature voltage. The characteristics are<br />
presented for quadrants I <strong>and</strong> II, accor<strong>di</strong>ng to figure 5.<br />
Also the power balance between the electrical <strong>and</strong><br />
mechanical powers is taken into consideration,<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 21
accor<strong>di</strong>ng to which Pel=Pm/η. The Simulink model of<br />
the BLDC-m is based on the above presented values.<br />
2.3 PHOTOVOLTAIC (PV) PANEL<br />
The PV panel is independent from the other<br />
components. It can be chosen so that it has maximum<br />
efficiency <strong>and</strong> a maintenance free robust structure.<br />
These requirements are all fulfilled with a crystalline,<br />
silicon on glass (CSG) 100 solar module manufactured<br />
by CSG <strong>Solar</strong> AG. Characteristics for the module are<br />
provided by the manufacturer in (CSG 2005) (see figure<br />
6). In (Ocran et al., 2005) one can find detailed<br />
calculation formulas about PV panels, but lack of<br />
detailed data makes not possible to perform calculations<br />
with these formulas. So, finally exponential functions<br />
were fitted on the characteristics considering their<br />
exponential like shape (see figure 6). The form of the<br />
fitted function is as follows:<br />
⎛ U−Umax ⎞<br />
T<br />
I0= K<br />
⎜<br />
1 −e<br />
U ⎟<br />
(4)<br />
⎜ ⎟<br />
⎜ ⎟<br />
⎝ ⎠<br />
I is the output current, U is the output voltage,<br />
Where 0<br />
Umax<br />
TU<br />
is the maximum possible output voltage, K <strong>and</strong><br />
are parameters to be calculated.<br />
Figure 6. PV panel characteristics from (CSG 2005)<br />
Calculations were performed for every insolation value<br />
(λ = 200÷1000 W/m 2 ), so K <strong>and</strong> T are insolation<br />
dependent. U is also insolation dependent, so<br />
max<br />
finally one can get the model fitting curves on K,<br />
<strong>and</strong> U using insolation as independent variable. For<br />
max<br />
Umax <strong>and</strong> U third order polynomials were used while<br />
K could be approximated with a single linear function.<br />
T<br />
U<br />
TU<br />
Another important aspect is the consideration of<br />
temperature effects in the model. This can be done<br />
using the temperature coefficient of power K P (CSG<br />
2005). With this, the PV panel output power should be<br />
corrected. In (Ocran et al., 2005) a maximum power<br />
point tracker controller for PV modules is derived, so<br />
one can assume that the PV module is operated always<br />
in the maximum efficiency region. This results in a<br />
working line considering insolation as independent<br />
variable. The U value at maximum power point ( U )<br />
is <strong>di</strong>fferent for <strong>di</strong>fferent insolation values, but a second<br />
degree polynomial describes it accurately.<br />
The final model for optimal PV panel power is as<br />
follows:<br />
P =<br />
PV<br />
⎛ Uopt<br />
( λ)<br />
−Umax<br />
( λ)<br />
⎞<br />
⎜<br />
⎟<br />
T ( )<br />
U ( ) ⋅ K(<br />
) ⋅ ⎜1<br />
− e U λ<br />
opt λ λ<br />
⎟ ⋅ (5)<br />
⎜<br />
⎟<br />
⎝<br />
⎠<br />
( 1+<br />
K P ( T − 25))<br />
Equation (5) describes correctly the PV panel power at<br />
<strong>di</strong>fferent insolation values, in maximum efficiency<br />
point with temperature correction (T is the actual cell<br />
temperature).<br />
2.4 BATTERY MODEL<br />
For battery modelling both simple <strong>and</strong> complicated<br />
solutions can be found in the literature.<br />
One should select the proper battery considering the<br />
modelling purposes. We have selected a relatively<br />
complex one, which models the battery as a real voltage<br />
generator considering the change in open circuit voltage<br />
when battery state of charge (SOC) changes. The sketch<br />
of this model is presented in figure 7.<br />
Figure 7. Battery model as real voltage generator<br />
The governing equations of this battery model are as<br />
follows:<br />
U = U + ( U −U<br />
) ⋅ SOC<br />
oc<br />
OC min<br />
OC max<br />
OC min<br />
2<br />
UOC<br />
− UOC<br />
− 4 ⋅ ( Rint<br />
+ Rt<br />
) ⋅ Pb<br />
Ib<br />
= −<br />
2 ⋅ ( Rint<br />
+ Rt<br />
)<br />
dSOC Q&<br />
Ib<br />
= =<br />
dt Qmax<br />
Qmax<br />
Pb<br />
Pb<br />
opt<br />
(6)<br />
In this type of formulation positive (battery power)<br />
means battery <strong>di</strong>scharge, while negative means<br />
battery charge.<br />
In (Koot et al., 2005) the efficiency of battery is also<br />
dealt with, which is modelled with the following<br />
expression:<br />
−5<br />
1 − 1−<br />
6 ⋅10<br />
⋅ Pbn<br />
P b =<br />
(7)<br />
−5<br />
3 ⋅10<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 22
Here Pbn<br />
means nominal battery power. The overall<br />
structure of the battery model, is presented in figure 8.<br />
Figure 8. Battery simulation structure<br />
The resultant battery model reflects all the important<br />
characteristics of a battery. The open circuit voltage<br />
decreases, when SOC decreases, the battery current<br />
calculation in (6) is asymmetric, which means that<br />
higher SOC rate can occur in <strong>di</strong>scharging than in<br />
charging. Nominal power ( Pbn<br />
) losses occur even in<br />
charging or <strong>di</strong>scharging mode.<br />
2.5 ELECTRIC GENERATOR AND INTERNAL<br />
COBUSTION ENGINE MODEL<br />
The electric generator <strong>and</strong> internal combustion engine<br />
(ICE) must be fitted to the electric motor <strong>and</strong> to each<br />
other. The selected electric motor with 58 kW<br />
maximum output mechanical power, needs maximum<br />
72.5 kW input electrical power. This must be provided<br />
by the electric generator if battery <strong>di</strong>scharge is not<br />
possible <strong>and</strong> the weather is cloudy (no insolation on PV<br />
panel). So, one has to select an electric generator that<br />
satisfies these requirements.<br />
Of course, the EG <strong>and</strong> ICE have to be fitted to each<br />
other using the maximum efficiency region for both of<br />
them. This way the EG can be described by a single<br />
characteristic curve, between input mechanical <strong>and</strong><br />
output electrical power as in figure 9.<br />
Figure 9. Electrical generator characteristic curve<br />
The description of ICE is possible in a similar way<br />
considering the maximum efficiency working line. The<br />
fuel map of the proper ICE (which can satisfy the EG<br />
input power needs) is depicted in figure 10.<br />
Figure 10. ICE fuel map<br />
In the fuel map, the fuel rate values are plotted against<br />
ICE torque <strong>and</strong> angular velocity values. Every<br />
combination of torque <strong>and</strong> angular velocity means a<br />
possible output power value for the motor. However,<br />
fuel rate is given at every point, from which input<br />
power can be calculated using the lower heat value of<br />
gasoline.<br />
The quotient of output <strong>and</strong> input power is the ICE<br />
efficiency. This way the efficiency map can be plotted<br />
against torque <strong>and</strong> angular velocity values (see figure<br />
11.). Of course, in points with zero input <strong>and</strong> output<br />
power efficiency can not be calculated so one can<br />
simply assume it to be zero.<br />
Figure 11. ICE efficiency map<br />
The determination of optimal working line is possible<br />
using a characteristic value mixed from output power<br />
<strong>and</strong> efficiency:<br />
opt = M ⋅ω<br />
⋅η<br />
(8)<br />
The goal is to find the trajectory which contains the<br />
maximum power points from zero, to maximum<br />
possible output power, with maximum efficiency. For<br />
this purpose the map of opt values can be used (see<br />
figure 12.)<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 23
Figure 12. Optimum variable map for ICE with optimal<br />
working line<br />
The optimal working line can be found with a gra<strong>di</strong>ent<br />
method, starting from the point (M = 0, ω = 0).<br />
Further, the next paragraph deals with MATLAB<br />
Simulink model construction using the component<br />
models.<br />
3 MATLAB SIMULINK MODEL CONSTRUCTION<br />
Model construction has multiple goals. The first goal is<br />
to create a model for simulation without controller,<br />
which gives an insight into the original characteristics<br />
of HSV. The second goal is model construction for<br />
controller design.<br />
Of course, the resultant model will be strongly<br />
nonlinear, so the linearization of model is required or<br />
nonlinear control techniques must be used.<br />
The model for initial vehicle simulations (backward<br />
calculations) can be seen in figure 13.<br />
Figure 13. Structure for basic HSV simulations<br />
In this model, one has to apply only a very simple<br />
control decision, which covers battery charging with<br />
regenerative braking.<br />
Tests were performed for the NEDC (figure 14) <strong>and</strong><br />
FUDS (figure 15) driving cycles, since these are the<br />
basic cycles used in urban traffic simulations.<br />
During calculations, the total fuel consumption <strong>and</strong><br />
final battery SOC were registered. Of course, the<br />
battery SOC has to increase because of regenerative<br />
braking <strong>and</strong> the lack of battery <strong>di</strong>scharge. The initial<br />
SOC value is 0.7 accor<strong>di</strong>ng to the literature (Musardo et<br />
al., 2005, Koot et al, 2005).<br />
Figure 14. New European Driving Cycle with time [s]<br />
on horizontal <strong>and</strong> velocity [km/h] on vertical axis<br />
Figure 15. Federal Urban Driving Schedule with time<br />
[s] on horizontal <strong>and</strong> velocity [km/h] on vertical axis<br />
Simulations were performed for <strong>di</strong>fferent insolation<br />
values. The resulting total fuel consumption data can be<br />
used as a reference for controller design, from which<br />
lower total consumptions have to be obtained. The<br />
results are summarized in table 2.<br />
λ [kW/m 2 ] 1 0.8 0.6 0.4 0.2 0<br />
SOC 0.7192 0.7189 0.7186 0.7183 0.7181 0.7178<br />
total fuel [g] 913.7265 916.015 918.1686 920.4583 922.613 924.768<br />
NEDC<br />
λ [kW/m 2 ] 1 0.8 0.6 0.4 0.2 0<br />
SOC 0.7125 0.7122 0.7119 0.7116 0.7113 0.711<br />
total fuel [g] 499.696 502.8127 505.7325 509.5911 513.0373 515.9575<br />
FUDS<br />
Table 2. Results from initial vehicle simulations<br />
As it can be seen in table 2, the total fuel consumptions<br />
increase, while the final SOC values decrease at lower<br />
insolation values. The total fuel <strong>and</strong> SOC trajectories<br />
for both drive cycles at maximum insolation are in<br />
figures 16-19.<br />
As a conclusion from these figures, one can state that<br />
FUDS does not contain sudden high changes in<br />
parameters, while the final part of NEDC contains<br />
strong changes. This results in strong changes in total<br />
fuel <strong>and</strong> SOC. The cause of this is the extra urban part<br />
of NEDC with a maximum speed of 120 km/h.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 24
Figure 16. Total fuel consumption trajectory, NEDC<br />
Figure 17. Total fuel consumption trajectory, FUDS<br />
Figure 18. SOC trajectory NEDC<br />
Figure 19. SOC trajectory FUDS<br />
This initial model can be a basis for optimal control<br />
input calculation with dynamic programming, while a<br />
slightly <strong>di</strong>fferent model should be constructed for other<br />
control design methods.<br />
For MPC control framework a forward calculation<br />
scheme is needed which can also be constructed from<br />
the component models.<br />
The selected model states, (control) inputs <strong>and</strong> outputs<br />
are:<br />
� State variables: - x1: ICE power state,<br />
- x2: SOC,<br />
� Inputs: - u1: ICE power,<br />
- x3: EM power state;<br />
- u2: Battery nominal power;<br />
� Controlled outputs: - o1: Drive power,<br />
- o2: SOC,<br />
- o3: Fuel rate;<br />
� Measured <strong>di</strong>sturbance input: - dm: PV panel<br />
power.<br />
The model can be linearized with MATLAB linmod or<br />
linmod2 functions. We have tested the resultant linear<br />
models <strong>and</strong> they were all controllable <strong>and</strong> observable so<br />
controller design for the HSV van is possible.<br />
4. CONCLUSIONS<br />
In this paper the control oriented modelling of<br />
components of a hybrid solar vehicle (HSV) <strong>and</strong> the<br />
overall vehicle structure was <strong>di</strong>scussed.<br />
Components are mainly modelled with their<br />
characteristics (EM, EG, ICE), with calculation<br />
formulas (vehicle dynamics <strong>and</strong> battery) or with<br />
formulas derived from the characteristics (PV panel).<br />
After component modelling the construction of two<br />
<strong>di</strong>fferent simulation structures in MATLAB Simulink<br />
was performed.<br />
The first model is for basic simulations <strong>and</strong> dynamic<br />
programming controller design, so it uses mainly<br />
backward calculation schemes. Only regenerative<br />
breaking is considered in it.<br />
The second model uses forward calculation which is<br />
proper for controller design in MPC framework. In this<br />
model the states, inputs <strong>and</strong> outputs are exactly defined.<br />
Simulations were performed only for the first model,<br />
generating reference total fuel consumption values for<br />
controller design. Of course, one has to get lower total<br />
fuel consumption from the controlled system. Results<br />
are summarized in table 2 for NEDC <strong>and</strong> FUDS drive<br />
cycles at several insolation values.<br />
ACKNOWLEDGEMENTS<br />
The authors gratefully acknowledge the contribution of<br />
Hungarian National Science foundation (OTKA N:<br />
K060767). This work was partially supported by the<br />
Hungarian National Office for Research <strong>and</strong><br />
Technology through the project "Advanced <strong>Vehicles</strong><br />
<strong>and</strong> Vehicle Control Knowledge Center" (no: OMFB -<br />
01418/2004).<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 25
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Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 26
SIMULATION PROGRAM AND CONTROLLER DEVELOPMENT FOR A 4WD<br />
PARALLEL HEV<br />
Ali Boyalı a , Murat Demirci a , Tankut Acarman b , Levent Güvenç a,*<br />
Burak Kıray c , Murat Yıldırım c<br />
a Istanbul Technical University, Department of Mechanical Engineering,<br />
Automotive Control <strong>and</strong> MechatronicsResearch Center <strong>and</strong> MEKAR Laboratories<br />
İnönü Cad. No:87 Gümüşsuyu, Taksim, TR-34437 İstanbul, Turkey<br />
b Galatasaray University, Faculty of Engineering <strong>and</strong> Technology, Computer Eng. Dept.,<br />
Çırağan Cad. No:36, TR-34357 Ortaköy, İstanbul, Turkey<br />
c Ford Otosan, İzmit Gölcük Yolu 14. Km, TR-41680 Gölcük, Kocaeli, Turkey<br />
Abstract: In this paper, we present a simulation model <strong>and</strong> a rule based controller design<br />
for a 4WD parallel HEV. A light commercial vehicle, equipped with inherited internal<br />
combustion engine, assembled with a battery pack, electrical actuator <strong>and</strong> its power<br />
converter is simulated by using the validated test results. A rule based controller <strong>and</strong> logic<br />
design is optimized to reduce fuel consumption <strong>and</strong> undesired emission with the assistance<br />
of the electrical actuator. Regenerative braking is shown to be capable of gaining back a<br />
certain percentage of the tire kinetic energy. The performance of the designed controller<br />
<strong>and</strong> logic switching between the two actuators are validated by experimental results.<br />
Copyright © 2006 IFAC<br />
Keywords: Control, modelling, design, rule-based systems, energy management systems<br />
1. INTRODUCTION<br />
Mass production of <strong>Hybrid</strong> Electric <strong>Vehicles</strong> (HEV)<br />
is becoming a global strategy for car manufacturers<br />
due to the prominent role of HEV in bringing down<br />
fossil fuel consumption <strong>and</strong> emissions. <strong>Hybrid</strong><br />
vehicles are a temporary solution on the way to the<br />
zero emission road vehicle. Toyota is planning to<br />
produce all its vehicles with hybrid technology by<br />
2012 (see Anonymous-a), <strong>and</strong> the sales volume of<br />
hybrid electric vehicles in the U.S. is expected to<br />
increase by 268 percent between the years 2005 <strong>and</strong><br />
2012 (see Anonymous-b).<br />
The effectiveness of fuel consumption depends not<br />
only on vehicle design but also on the control<br />
strategy used. Several HEV control strategies have<br />
been proposed in the open literature. The underlying<br />
methodology in HEV control is to find the optimum<br />
power split ratio between the two power sources. The<br />
simplest <strong>and</strong> easiest to adapt control method is the<br />
rule based control algorithm (see for ex. Boyalı, et al,<br />
2006). In this algorithm, the vehicle states are<br />
detected <strong>and</strong> the control comm<strong>and</strong>s are generated<br />
based on rules correspon<strong>di</strong>ng to the particular state.<br />
Rules are constructed based on engineering intuition<br />
<strong>and</strong> rigorous analyses of fuel consumption <strong>and</strong><br />
emission maps belonging to the internal combustion<br />
engine (ICE), rather than analytical computation of<br />
optimum operating points based on minimization of a<br />
cost function. In some HEV applications,<br />
deterministic optimal control is applied, (see Lin, et<br />
al., 2003). For a given speed profile, the global<br />
optimum operation paths of vehicle components may<br />
be calculated using the dynamic programming<br />
method. However, in real-time driving con<strong>di</strong>tions,<br />
*<br />
Correspon<strong>di</strong>ng author, Prof.Dr. Levent Güvenç<br />
E-mail addresses : guvencl@itu.edu.tr<br />
URL : http://mekar.itu.edu.tr<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 27
the speed profile is not known a priori <strong>and</strong> a global<br />
minimum can not be determined. The remedy is to<br />
find sub-optimal solutions approaching the global<br />
optimum. One of these suboptimal methods is to<br />
compute equivalent fuel consumption <strong>and</strong> to evaluate<br />
power split ratio instantaneously to minimize a<br />
chosen cost function (Sciarretta, et al., 2004;<br />
Paganelli, et al., 2001a; Paganelli, et al., 2001b;<br />
Johnson, et al., 2000). Another approach is to apply<br />
stochastic optimal control methods in the short time<br />
intervals while pre<strong>di</strong>cting the speed profile of the<br />
controlled HEV (Jeon, et al., 2001).<br />
This paper <strong>di</strong>scusses the modeling <strong>and</strong> control of a<br />
four wheel drive hybrid electric vehicle <strong>and</strong><br />
experimental test results. An explanation of the<br />
simulation model structure is given in section II. In<br />
sections III, the control algorithm involving vehicle<br />
states, transition states <strong>and</strong> switching logic between<br />
two actuators are explained. In Section IV, the<br />
hardware setup integrated into the experimental<br />
vehicle for performing the proposed control<br />
algorithm on a real-time basis is presented.<br />
Simulation results are demonstrated in section V.<br />
Experimental results are given in section VI. The<br />
paper ends with conclusions.<br />
2. VEHICLE MODEL<br />
In this study, a four wheel drive Ford Transit<br />
commercial van is modeled using the<br />
Matlab/Simulink toolbox. Since rear <strong>and</strong> front wheel<br />
drive vans were commercially available, the<br />
experimental vehicle was formed by combining these<br />
two drive axles in one vehicle. The result was a four<br />
wheel drive (4WD) hybrid electric vehicle. The front<br />
drive is powered by the internal combustion engine<br />
<strong>and</strong> the rear drive is powered by the electric motor. A<br />
first prototype HEV of this construction was<br />
explained in our previous work in Boyalı, et al, 2006.<br />
This paper concentrates on a second prototype<br />
vehicle based on this 4WD concept, referred to as the<br />
experimental vehicle hereafter.<br />
Modeling of this experimental vehicle is presented<br />
first. The equations of dynamics for the considered<br />
model may be found in Boyalı, et al, 2006. The<br />
Simulink implementation of the model is shown in<br />
Fig. 1.<br />
Fig. 1. Simulink vehicle model<br />
This model consists mainly of six blocks. These<br />
blocks are the longitu<strong>di</strong>nal vehicle model, nonlinear<br />
tire model, internal combustion engine model,<br />
electric motor (EM) model, driver model <strong>and</strong><br />
supervisory controller.<br />
The net longitu<strong>di</strong>nal force acting on the vehicle is<br />
used to compute vehicle acceleration by subtracting<br />
the resistance forces such as aerodynamic, rolling<br />
resistance <strong>and</strong> the resistance induced by road slope,<br />
from the traction forces that are available from the<br />
tire blocks. The Pajecka 2002 tire equations are used<br />
for modeling the tire. Although the tire model is<br />
capable of computing all tire forces <strong>and</strong> moments,<br />
only longitu<strong>di</strong>nal forces are utilized in this model.<br />
The lateral forces <strong>and</strong> moments can be used for<br />
further stu<strong>di</strong>es such as hybrid vehicle lateral stability<br />
analysis due to the fact that the established model is<br />
modular in structure.<br />
The engine is modeled using engine maps that give<br />
the output engine torque for the two inputs of engine<br />
speed <strong>and</strong> accelerator pedal position. Transient<br />
regimes of the engine are thus not treated. Negative<br />
engine torque is computed to introduce function of<br />
cylinder head temperature <strong>and</strong> instantaneous engine<br />
speed.<br />
Transmission components are assumed to be rigid<br />
bo<strong>di</strong>es, only equivalent inertias <strong>and</strong> transmission<br />
ratios are used to model the driveline. Even though<br />
the efficiency of transmission components varies<br />
with respect to transmission speed, gear ratio <strong>and</strong> the<br />
torque, constant efficiency values are used for<br />
computational simplicity.<br />
For a given speed profile, the driver model accepts<br />
the desired speed <strong>and</strong> actual speed as its two inputs.<br />
Anti-windup Proportional-Integral (PI) controllers<br />
are used to model the driver <strong>and</strong> to comm<strong>and</strong> the ICE<br />
<strong>and</strong> EM. Two feedback options are available. Speed<br />
feedback is not suitable for controlling the 4WD<br />
vehicle since the rear <strong>and</strong> front axle dynamics require<br />
<strong>di</strong>fferent torques due to the <strong>di</strong>fferent component<br />
properties. Thus, torque feedback is used to follow<br />
the desired speed profile. Once the desired speed<br />
starts to increase, the controller sends the throttle<br />
signal to the engine. Ad<strong>di</strong>tionally, the driver model<br />
generates clutch <strong>and</strong> brake signals. To imitate the<br />
real clutch-engine relation for the EM only state, <strong>and</strong><br />
to improve driving feeling while shifting gears with<br />
respect to conventional ICE vans, a potentiometer<br />
that generates a linear signal between “0” <strong>and</strong> “1” is<br />
used in the experimental vehicle.<br />
Look-up tables inclu<strong>di</strong>ng data of braking torque<br />
versus brake pedal position are used for modeling the<br />
brakes. In order not to change braking characteristics<br />
of the vehicle, a force gap is allocated for<br />
regenerative braking. Along this gap, only<br />
regenerative braking is allowed. In designing<br />
regenerative braking, the regulations on braking are<br />
also taken into account. After a certain amount of<br />
applied pedal force, conventional friction brakes are<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 28
activated <strong>and</strong> the regenerative braking torque is<br />
decreased gradually as illustrated in Fig. 2.<br />
A simple equivalent circuit is used as the battery<br />
model. The open circuit voltage <strong>and</strong> internal<br />
resistance depen<strong>di</strong>ng on state of charge <strong>and</strong> current<br />
flow <strong>di</strong>rection are used to build the necessary<br />
equations. For simplification of the overall electric<br />
traction system modeling, a permanent magnet <strong>di</strong>rect<br />
current motor model is used (see Boyalı, et al, 2006).<br />
Fig. 2. Regenerative braking characteristics<br />
3. RULES AND FINE TUNING<br />
The main aim of introducing rule based control is to<br />
operate the ICE at high loads which correspond to its<br />
efficient regions. For this reason, the electric motor<br />
(EM) only mode operates under a predetermined<br />
driver power request <strong>and</strong> also when <strong>di</strong>rect EM<br />
assistance is desired by the driver during gas pedal<br />
kick-down. The required power to drive the vehicle<br />
is computed for a given drive cycle. In real-time<br />
driving con<strong>di</strong>tions, driver power or torque request at<br />
the wheels is computed by evaluating the accelerator<br />
pedal position <strong>and</strong> brake pedal force rea<strong>di</strong>ng.<br />
Measured values are used in the ICE torque <strong>and</strong><br />
brake maps <strong>and</strong> correspon<strong>di</strong>ng positive or negative<br />
desired torques are calculated.<br />
There are five main vehicle states in the control<br />
algorithm which are, see (Fig. 3).<br />
• St<strong>and</strong>still vehicle position (St<strong>and</strong>still mode)<br />
• Pure EM excitation (EM mode)<br />
• Pure ICE excitation (ICE mode)<br />
• Charging or EM assist (<strong>Hybrid</strong> mode)<br />
• Braking mode (regenerative <strong>and</strong> conventional<br />
friction braking)<br />
To decide which state will be active, some transition<br />
rules are used. If the vehicle speed is below a small<br />
value such as 5 km/h, the vehicle is assumed to be in<br />
st<strong>and</strong>still position. Other state transitions are<br />
determined accor<strong>di</strong>ng to the logic rules given in<br />
Table I. To avoid limit cycle oscillations, hysteresis<br />
is added to the transitions.<br />
Fig. 3. Vehicle states<br />
Table 1. Transition Logic<br />
Traction torque is supplied by the EM in the pure<br />
EM mode where the ICE follows the wheel speed.<br />
Since the manual clutch can not be comm<strong>and</strong>ed<br />
automatically, the engine compression brake<br />
becomes active as shown in Fig. 4. This is an<br />
inherited <strong>di</strong>sadvantage of the experimental vehicle<br />
towards HEV real-time operation as the EM should<br />
meet both the driver request <strong>and</strong> engine compression<br />
brake during the EM only mode. This drawback is<br />
Vehicle<br />
Speed<br />
State of<br />
Charge<br />
Requested<br />
Power.<br />
Max. ICE Torque Max. EM Torque<br />
Brake Pedal<br />
Force<br />
St<strong>and</strong>still SOClow < 6 kW --<br />
< Requested.<br />
Torque<br />
--<br />
Pure ICE -- < SOClow < 6 kW -- -- --<br />
Pure ICE -- > SOClow > 7 kW > Requested. Torque -- --<br />
EM Assist -- > SOClow -- < Requested. Torque -- --<br />
EM Generator -- < SOClow --<br />
= SOChigh -- -- -- --<br />
Conv. Braking -- < SOChigh -- -- -- > 90<br />
compensated since the engine cuts off fuel while<br />
braking.<br />
Another <strong>di</strong>fficulty is to keep drivability of the hybrid<br />
electric vehicle at the same level as the conventional<br />
vehicle in the presence of a manual clutch. This can<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 29
e compensated by using appropriate transition<br />
functions between pure ICE <strong>and</strong> pure EM states <strong>and</strong><br />
by using the clutch potentiometer to sense clutch<br />
position.<br />
Fig. 4. Engine torque map<br />
The transition function is a function of the torque<br />
supplied by the power source at the wheels <strong>and</strong> time.<br />
If the transition con<strong>di</strong>tions are realized between ICE<br />
<strong>and</strong> EM, the vehicle enters into the transition states<br />
(Fig 5.).<br />
Fig. 5. Transition states<br />
During the transition states, the instantaneous<br />
required torque at the wheels is supplied by both<br />
power sources. For instance the EM power starts to<br />
decrease linearly as the ICE power increases linearly<br />
to keep on supplying the required power (Fig. 6.).<br />
Fig. 6. EM <strong>and</strong> ICE torques in transition states<br />
Since the total torque always equals the dem<strong>and</strong>ed<br />
torque, the driver does not feel an abrupt transition.<br />
The change is smooth <strong>and</strong> is not noticed by the<br />
driver. To avoid unwanted oscillations such as shunt<br />
<strong>and</strong> shuffle during the transitions, the dem<strong>and</strong>ed<br />
torque, engine torque <strong>and</strong> EM torque at the wheels<br />
are computed as accurately as possible. This is<br />
obviously an open loop control approach which uses<br />
available offline data. If an accurate engine map, i.e.,<br />
torque output versus ICE speed, is available, an<br />
inverse map can be used to <strong>di</strong>stribute required torque<br />
between the EM <strong>and</strong> the ICE. Another easier<br />
approach is to calibrate the accelerator pedal position<br />
in such a way that the EM generates the same<br />
amount of torque as the ICE for the same pedal<br />
position (Boyalı, et al, 2006).<br />
The current transmission stick shift position also has<br />
to be estimated in real time in order to compute the<br />
torque dem<strong>and</strong> at the wheels. Vehicle speed <strong>and</strong><br />
wheel angular speeds are available on the CAN bus.<br />
The ratio of these two speeds gives the transmission<br />
gear ratio <strong>and</strong> thus the stick shift position. There are<br />
upper <strong>and</strong> lower variations for each gear ratio as<br />
plotted in Fig. 7. The gear position estimation is<br />
carried out using a Stateflow <strong>di</strong>agram in Simulink.<br />
Fig. 7. Gear ratio variations<br />
4. HARDWARE SETUP<br />
A dSpace MicroAutoBox (MABX) complemented<br />
with a RapidPro system is used as the main<br />
electronic control unit to carry out the HEV control<br />
algorithm. The MABX <strong>and</strong> Rapidpro system<br />
installed in the Ford Transit van is shown in Fig. 8.<br />
Fig. 8. HEV controller hardware connections in the<br />
experimental vehicle<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 30
All signals required by the HEV controller are<br />
gathered via the MABX <strong>and</strong> the RapidPro signal<br />
con<strong>di</strong>tioning units. Vehicle <strong>and</strong> battery states are<br />
monitored via rea<strong>di</strong>ng the CAN bus. The other<br />
signals are analog signals. The general signal<br />
connection <strong>di</strong>agram is shown in Fig. 9.<br />
The HEV control strategy is modeled in<br />
Matlab/Simulink. Automatic code generation <strong>and</strong><br />
downloa<strong>di</strong>ng into MABX is achieved by the Matlab<br />
Real Time Workshop <strong>and</strong> dSpace Real Time<br />
Interface tools as illustrated in Fig. 10.<br />
Fig. 9. General signal connection <strong>di</strong>agram<br />
Fig. 10. Rapid HEV control algorithm prototyping<br />
process <strong>di</strong>agram<br />
Following the electrical <strong>and</strong> mechanical flows<br />
plotted in Fig. 11, the EM driver enables the<br />
conversion of DC voltage to AC voltage. The electric<br />
power is supplied by a battery pack which is<br />
connected to the motor driver through a circuit<br />
breaker as a safety switch. The available EM driver<br />
control signals (enable, <strong>di</strong>rection, acceleration,<br />
brake) allow smooth operation of the EM via its<br />
driver. The HEV control unit sends the comm<strong>and</strong>s to<br />
the controller as acceleration or brake requests. The<br />
EM driver applies these requests accor<strong>di</strong>ng to the<br />
motor operating region or generator operating region<br />
maps.<br />
Fig. 11. EM electrical <strong>and</strong> mechanical connections<br />
(Boyalı, et al, 2006).<br />
5. SIMULATION RESULTS WITH POWER-<br />
ORIENTED CONTROL RULES<br />
The EUDC drive cycle is used in simulation to<br />
compute fuel consumption <strong>and</strong> emitted emission<br />
quantities. The results are listed in Table II for a<br />
vehicle mass of 3000 kg. Emission values given in<br />
Table II are the engine-out emissions. SOC is short<br />
for state of charge of the batteries<br />
Table 2. Fuel Consumption <strong>and</strong> Emissions<br />
Conven. <strong>Hybrid</strong> Improv.<br />
Fuel Consp.<br />
Litre/100 km<br />
11 9.3 % 15.5<br />
Δ SOC % -- 0 --<br />
NOx -- gr/km 0.77 0.55 % 28<br />
CO2 -- gr/km 2.76 2.26 % 18<br />
CO-- gr/km 5 4.75 % 5<br />
Acceleration tests are also performed. For this<br />
reason, a gear shift algorithm pertaining to this<br />
vehicle is necessary. To determine the gear up shift<br />
points, the torque versus engine speed curves at the<br />
wheels were drawn for each gear (Fig. 12). The<br />
intersections of the curves are the gear shift points<br />
that maximize the area <strong>and</strong> thus acceleration<br />
performance under these curves. If this is repeated<br />
for each accelerator position with a specified<br />
increment, the gear shift graph in Fig. 13 is obtained.<br />
In hybrid acceleration tests, the EM operates in the<br />
assist mode accor<strong>di</strong>ng to the rule based control<br />
algorithm. As the pedal opening exceeds 70% of its<br />
full travel range, the EM starts to give assist torque<br />
linearly.<br />
Acceleration simulation results are given Table III<br />
<strong>and</strong> Figures 14-15.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 31
Fig 12. Engine torque versus vehicle speed<br />
Fig. 13. Optimal gear shift curves for acceleration<br />
performance<br />
Table III. Conventional <strong>and</strong> <strong>Hybrid</strong> Vehicle Acceleration<br />
Performances<br />
Conventional [s] <strong>Hybrid</strong> [s]<br />
8-32,3 km/h 2.086 2.08<br />
8-56,4 km/h 5.6 5.6<br />
0-100 km/h 22.37 17.13<br />
80-120 km/h 18,76 12.34<br />
Fig 14. Simulated <strong>Hybrid</strong> <strong>and</strong> Conventional Vehicle<br />
acceleration performances<br />
Fig 15. Simulated engine speed <strong>and</strong> gear position<br />
history<br />
6. EXPERIMENTAL RESULTS AND MODEL<br />
VERIFICATION<br />
Accelerator, brake, clutch pedal <strong>and</strong> gear positions<br />
were recorded during an experimental acceleration<br />
test <strong>and</strong> were used as inputs to the simulation model<br />
in a subsequent simulation study.<br />
The experimental <strong>and</strong> simulation results are<br />
<strong>di</strong>splayed in Figures 16 <strong>and</strong> 17. The simulated <strong>and</strong><br />
real test results, with their close matching, show the<br />
effectiveness of the proposed simulation modelling<br />
approach. The HEV control algorithm states entered<br />
in the acceleration test are shown in Fig. 18.<br />
Fig 16. Conventional vehicle acceleration<br />
comparison of simulated <strong>and</strong> experimental<br />
responses<br />
Fig 17. <strong>Hybrid</strong> vehicle acceleration comparison of<br />
simulated <strong>and</strong> experimental responses<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 32
Fig 18. Vehicle speed <strong>and</strong> states during test drive<br />
During driving tests, the state of charge of the<br />
vehicle was also recorded <strong>and</strong> is shown in Fig. 19. In<br />
the charge state, the torque request of the driver is<br />
evaluated <strong>and</strong> a charge torque is calculated within<br />
component constraints. Since the ICE meets both the<br />
driver torque dem<strong>and</strong> <strong>and</strong> the charge torque in the<br />
charge mode, the driver does not feel a significant<br />
change with respect to the conventional vehicle.<br />
Fig 19. SOC change in Charge state<br />
7. CONCLUSIONS<br />
Two vehicles were successively converted into<br />
hybrid electric vehicles <strong>and</strong> instrumented with a<br />
battery, an electric motor <strong>and</strong> sensors. The second<br />
experimental vehicle is shown in Fig. 20. A<br />
simulation model <strong>and</strong> its use in designing a rule<br />
based control algorithm were presented. Simulation<br />
<strong>and</strong> experimental results were compared to<br />
demonstrate the vali<strong>di</strong>ty of the results achieved.<br />
Future work will concentrate on the use of local <strong>and</strong><br />
global optimization methods.<br />
Fig. 20 Ford Transit Van <strong>and</strong> battery packs<br />
ACKNOWLEDGEMENT<br />
The authors acknowledge the support of Ford Otosan<br />
R&D Department <strong>and</strong> the European Union<br />
Framework Programme 6 project INCO-16426.<br />
REFERENCES<br />
Anonymous-a, http://www.automotive<strong>di</strong>gest.com.<br />
Anonymous-b, http://www.jdpower.com.<br />
Boyalı A., Demirci M., Acarman T., Güvenç L., Kiray B., Özatay<br />
E. (2006), Modeling <strong>and</strong> Control of a Four Wheel Drive<br />
Parallel <strong>Hybrid</strong> Electric Vehicle, Procee<strong>di</strong>ngs of the IEEE<br />
Conference on Control Applications, Munich, Germany,<br />
November (to appear).<br />
Lin C. C., Peng H., Grizzle J.W., <strong>and</strong> Kang J.M. (2003), Power<br />
Management Strategy for a Parallel <strong>Hybrid</strong> Electric Truck,<br />
IEEE Transaction on Control Systems Technology, Vol. 11,<br />
No. 6. pp 849-839,<br />
Sciarretta A., Back M., <strong>and</strong> Guzzella L., Optimal Control of<br />
Parallel <strong>Hybrid</strong> Electric <strong>Vehicles</strong> (2004), IEEE Transactions<br />
on Control Systems Technology, Vol. 12, No:3. pp. 352-363.<br />
Paganelli G., Ercole G., Brahma A., Guezennec Y., Rizzoni G.<br />
(2001), General Supervisory Control Policy for the Energy<br />
Optimization of Charge-Sustaining <strong>Hybrid</strong> Electric <strong>Vehicles</strong>,<br />
JSAE Review, Vol. 22, pp. 511–518<br />
Paganelli G., Delprat S., Guerra T.M., Rimaux J., Santin J.J.,<br />
(2001), Equivalent Consumption Minimization Strategy for<br />
Parallel <strong>Hybrid</strong> Powertrains, Procee<strong>di</strong>ngs of Vehicular<br />
Transportation Systems Conference, Atlantic City, NJ, USA.<br />
Johnson V. H., Wipke K.B., <strong>and</strong> Rausen D.J. (2001), HEV Control<br />
Strategy for Real-Time Optimization of Fuel Economy <strong>and</strong><br />
Emissions, SAE 2000-01-1543.<br />
S. Jeon, K.B. Kim, S.T. Jo, <strong>and</strong> J.M. Lee (2001), Driving<br />
Simulation of a Parallel <strong>Hybrid</strong> Electric Vehicle Using<br />
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Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 33
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 34
A MODEL FOR A HYBRID SOLAR VEHICLE PROTOTYPE<br />
Ivan Arsie, Raffaele Di Martino, Gianfranco Rizzo, Marco Sorrentino<br />
Department of Mechanical Engineering, University of <strong>Salerno</strong>, 84084 Fisciano (SA), Italy<br />
Abstract: The paper deals with a dynamic model for the simulation of a solar hybrid<br />
prototype, developed in the framework of the Leonardo Program I05/B/P/PP-154181.<br />
The model is based on a longitu<strong>di</strong>nal vehicle dynamic model <strong>and</strong> allows evaluating the<br />
effects of solar panels area <strong>and</strong> position, vehicle <strong>di</strong>mensions <strong>and</strong> propulsion system<br />
components on vehicle performance, weight, fuel savings, autonomy <strong>and</strong> costs.<br />
Simulation results show that significant fuel savings vs. conventional vehicle powered by<br />
internal combustion engine can be achieved for intermittent use in urban area <strong>and</strong> that<br />
economic feasibility could be achieved in the next future, considering the expected trends<br />
in costs <strong>and</strong> prices. Furthermore the hybrid series architecture allows increasing<br />
significantly vehicle autonomy vs. pure electrical vehicle.<br />
Keywords: modeling, simulation analysis, hybrid solar vehicles, photovoltaic energy,<br />
control.<br />
1. INTRODUCTION<br />
In the last years, increasing attention has been spent<br />
towards the applications of solar energy to cars.<br />
Various solar car prototypes have been built <strong>and</strong><br />
tested, mainly for racing <strong>and</strong> demonstrative purposes<br />
[1].<br />
Despite a significant technological effort <strong>and</strong> some<br />
spectacular outcomes, several limitations, such as low<br />
power density, unpre<strong>di</strong>ctable availability of solar<br />
source <strong>and</strong> energetic drawbacks, cause pure solar cars<br />
to be still far from practical feasibility. On the other<br />
h<strong>and</strong>, the concept of a hybrid electric car assisted by<br />
solar panels appears more realistic [3][4][5][6][7]. In<br />
fact, due to relevant research efforts [8], in the last<br />
decades <strong>Hybrid</strong> Electric <strong>Vehicles</strong> (HEV) have<br />
evolved to industrial maturity. These vehicles now<br />
represent a realistic solution to important issues, such<br />
as the reduction of gaseous pollution in urban drive as<br />
well as the energy saving requirements. Moreover,<br />
there is a large number of drivers utilizing daily their<br />
car, for short trips <strong>and</strong> with limited power dem<strong>and</strong>.<br />
Some recent stu<strong>di</strong>es, conducted by the UK<br />
government, report that about 71 % of UK users reach<br />
their office by car, <strong>and</strong> 46 % of them have trips<br />
shorter than 20 minutes, mostly with only one<br />
passenger (i.e. the driver) [9]. The above<br />
considerations open promising perspectives on the<br />
integration of solar panels with “pure”-electric hybrid<br />
vehicles (i.e. “tri-hybrid” cars), with particular<br />
interest in the opportunity of storing energy even<br />
during parking phases.<br />
In spite of their potential interest, solar hybrid cars<br />
have received relatively little attention in literature<br />
[7]. An innovative prototype has been developed at<br />
Western Washington University [5][6] in the 90s,<br />
adopting advanced solutions for materials,<br />
aerodynamic drag reduction <strong>and</strong> PV power<br />
maximization with peak power tracking. Other<br />
stu<strong>di</strong>es <strong>and</strong> prototypes on solar hybrid vehicles have<br />
been presented by Japanese researchers [3][4] <strong>and</strong> at<br />
the Queensl<strong>and</strong> University [10].<br />
Although these works demonstrate the general<br />
feasibility of such an idea, detailed presentation of<br />
results <strong>and</strong> performance, along with a systematic<br />
approach to solar hybrid vehicle design, seem still<br />
missing in literature. Therefore, appropriate<br />
methodologies are required to address both the rapid<br />
changes in the technological scenario <strong>and</strong> the<br />
increasing availability of innovative, more efficient<br />
components <strong>and</strong> solutions. A specific <strong>di</strong>fficulty in<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 35
developing a <strong>Hybrid</strong> <strong>Solar</strong> Vehicle (HSV) model<br />
relates to the many mutual interactions between<br />
energy flows, power-train balance of plant <strong>and</strong> sizing,<br />
vehicle <strong>di</strong>mension, performance, weight <strong>and</strong> costs,<br />
whose connections are much more critical than in<br />
either conventional or hybrid electric vehicles.<br />
The current study focuses on the extension of the<br />
analysis methodologies presented in [11][12][18] to a<br />
hybrid solar vehicle prototype, now under<br />
development at DIMEC – University of <strong>Salerno</strong>. This<br />
activity is being conducted in the framework of the<br />
UE funded Leonardo project I05/B/P/PP-154181<br />
“Energy Conversion Systems <strong>and</strong> Their<br />
Environmental Impact” [17]. The on going research is<br />
also extended to the study of real time control of solar<br />
panels (MPPT techniques <strong>and</strong> their implementation)<br />
<strong>and</strong> to the development of converters specifically<br />
suited for automotive applications [19].<br />
2. THE SOLAR HYBRID VEHICLE MODEL<br />
Different architectures can be applied to HEVs:<br />
series, parallel, <strong>and</strong> parallel-series. The two latter<br />
structures have been utilized for two of the more<br />
widely available hybrid cars in the market: Toyota<br />
Prius (parallel-series) <strong>and</strong> Honda Civic (parallel).<br />
Instead, for solar hybrid vehicles the series structure<br />
seems preferable [7], due to its simplicity, as in some<br />
recent prototypes of HSV [10]. With this approach,<br />
the Photovoltaic Panels (PV) assist the Electric<br />
Generator EG, powered by the Internal Combustion<br />
Engine (ICE), in recharging the Battery pack (B) in<br />
both parking mode <strong>and</strong> driving con<strong>di</strong>tions, through<br />
the Electric Node (EN). The Electric Motor (EM) can<br />
either provide the mechanical power for the<br />
propulsion or restore part of the braking power during<br />
regenerative braking (Figure 1). In this structure, the<br />
thermal engine can work mostly at constant power<br />
(Pav), correspon<strong>di</strong>ng to its optimal efficiency, while<br />
the electric motor EM can reach a peak power PEM:<br />
P ⋅<br />
EM = θ Pav<br />
(1)<br />
EG<br />
ICE<br />
PV<br />
EN<br />
B<br />
EM<br />
Figure 1 - Scheme of the series hybrid solar<br />
vehicle.<br />
2.1 <strong>Solar</strong> energy for vehicle propulsion<br />
In order to estimate the net solar energy captured by<br />
PV panels in real con<strong>di</strong>tions (i.e. considering clouds,<br />
rain etc.) <strong>and</strong> available for propulsion, a solar<br />
calculator developed at the US National Renewable<br />
Energy Lab has been used [12]. Four <strong>di</strong>fferent US<br />
locations were considered, ranging from 21° to 61° of<br />
latitude, based on 1961-1990 time series. The<br />
calculator provides the net solar energy for <strong>di</strong>fferent<br />
panel positions: with 1 or 2 axis tracking mechanism<br />
or for fixed panels, at various tilt <strong>and</strong> azimuth angles.<br />
The most obvious solution for solar cars is the<br />
location of panels on roof <strong>and</strong> bonnet, at almost<br />
horizontal position. Nevertheless, two ad<strong>di</strong>tional<br />
options can be accounted for: (i) horizontal panels (on<br />
roof <strong>and</strong> bonnet) with one tracking axis, in order to<br />
maximize the energy captured during parking mode;<br />
(ii) panels located also on car sides <strong>and</strong> rear at almost<br />
vertical positions. The maximum panel area can be<br />
estimated as function of car <strong>di</strong>mensions <strong>and</strong> shape, by<br />
means of a simple geometrical model. An analysis of<br />
the effect of panel position at <strong>di</strong>fferent latitudes has<br />
been presented recently by the authors [11].<br />
The energy from PV panels can be obtained summing<br />
up the contribution from parking (p) <strong>and</strong> driving (d)<br />
periods. While in the former case it is reasonable to<br />
assume that the PV array has an unobstructed view of<br />
the sky, this hypothesis could fail in most driving<br />
con<strong>di</strong>tions. Therefore, the energy captured during<br />
driving can be reduced by a factor β
efficiency, aerodynamic losses (CX, cross section)<br />
<strong>and</strong> weight.<br />
Thus, the required driving energy Ed depends on<br />
vehicle weight <strong>and</strong> aerodynamic parameters, which in<br />
turn depend on the sizing of the propulsion system<br />
components <strong>and</strong> on vehicle <strong>di</strong>mensions, related to<br />
solar panel area.<br />
Battery, electric motor <strong>and</strong> generator have been<br />
simulated by the ADVISOR model [16].<br />
2.2 Vehicle weight<br />
The parametric weight model of the HSV can be<br />
obtained ad<strong>di</strong>ng the weight of the specific<br />
components (PV panels, battery pack, ICE,<br />
Generator, Electric Motor, Inverter) to the weight of<br />
the Conventional Vehicle (CV) equipped with ICE<br />
(WCV) <strong>and</strong> by subtracting the contribution of the<br />
components not present in the HSV (i.e. ICE,<br />
gearbox, clutch).<br />
Thus, the body (i.e. Wbody,HSV) <strong>and</strong> whole (WHSV) mass<br />
of the HSV can be expressed as:<br />
( w w )<br />
W +<br />
W<br />
body , HSV = WCV<br />
− PICE<br />
, CV ⋅ ICE gear (5)<br />
HSV<br />
= W<br />
+ P<br />
+ A<br />
body,<br />
HSV<br />
EG<br />
PV<br />
+<br />
wICE<br />
⋅ + PEG<br />
⋅ w<br />
ηEG<br />
w + w ⋅ N<br />
PV<br />
B,<br />
u<br />
B<br />
EG<br />
+ P<br />
EM<br />
w<br />
EM<br />
(6)<br />
Considering the lay-out described in Figure 1, the<br />
required nominal battery power is:<br />
P = P − P<br />
(7)<br />
B<br />
EM<br />
EG<br />
Therefore the number of battery modules is evaluated<br />
as:<br />
N<br />
P<br />
− P<br />
EM EG<br />
B = (8)<br />
PB<br />
, u<br />
where PB,u is the nominal power of a single battery<br />
module. The power of the electric machine (PEM) is<br />
computed imposing that the HSV Power to Weight<br />
ratio (PtWHSV) equals the Power to Weight ratio of the<br />
reference vehicle:<br />
PtW<br />
EM<br />
P<br />
= (9)<br />
ICE,<br />
CC<br />
HSV<br />
Wbody,<br />
CC<br />
P = PtW ⋅W<br />
(10)<br />
HSV<br />
2.3 Cost estimation<br />
HSV<br />
In order to assess the benefits provided by HSV with<br />
respect to conventional vehicles, both the ad<strong>di</strong>tional<br />
costs, due to hybri<strong>di</strong>zation <strong>and</strong> solar panels, <strong>and</strong><br />
achievable fuel savings are to be estimated. The<br />
ad<strong>di</strong>tional cost CHSV can be expressed starting from<br />
the estimated unit cost of each component:<br />
C<br />
HSV<br />
cICE<br />
= PEG<br />
⋅ + PEG<br />
⋅ cEG<br />
+ APVc<br />
PV<br />
η EG<br />
(11)<br />
+ P c + C N − P ⋅ c<br />
max<br />
EM<br />
B<br />
B<br />
ICE , CV<br />
ICE<br />
The last term accounts for cost reduction for Internal<br />
Combustion Engine in HSV (where it is assumed PICE<br />
= PEG/ηEG) with respect to conventional vehicle<br />
(where PICE = PICE,CV). The daily saving with respect<br />
to conventional vehicle can be computed starting<br />
from fuel saving <strong>and</strong> fuel unit cost:<br />
( m f CC − m f HSV ) c f<br />
S ⋅<br />
= , ,<br />
(12)<br />
The pay-back, in terms of years necessary to restore<br />
the ad<strong>di</strong>tional costs with respect to the conventional<br />
vehicle, can be therefore estimated as:<br />
CHSV<br />
CHSV<br />
PB = =<br />
(13)<br />
n S 300S<br />
D<br />
For further details about the meaning <strong>and</strong> the values<br />
of some of the parameters introduced in eqs. 2<br />
through 13, the reader is addressed to previous work<br />
[11] [18].<br />
3. ENGINE CONTROL FOR HSV<br />
In most electric hybrid vehicles, a charge sustaining<br />
strategy is adopted: at the end of a driving path, the<br />
battery state of charge should remain unchanged.<br />
With a solar hybrid vehicle, a <strong>di</strong>fferent strategy<br />
should be adopted as battery is charged during<br />
parking hours as well. In this case, a <strong>di</strong>fferent goal<br />
can be pursued, namely restoring the initial state of<br />
charge within the end of the day rather than after a<br />
single driving path [12] [18]. For this end, the internal<br />
combustion engine should be operated whenever<br />
possible at maximum efficiency, correspon<strong>di</strong>ng to<br />
power Popt. If the energy required to restore battery<br />
charge is lower than the amount correspon<strong>di</strong>ng to a<br />
continuous use at Popt throughout the driving time hd<br />
(case B), an intermittent operation can be adopted<br />
(cases A1-A2). In case that more energy is required,<br />
the internal combustion engine is operated at constant<br />
power between Popt <strong>and</strong> Pmax (case C). The <strong>di</strong>fferent<br />
operating modes can be described by the variable φ,<br />
ranging from 0 to φmax = Pmax / Popt, as described in<br />
Tab. I.<br />
The optimal φ value is found by imposing that the<br />
energy provided by ICE <strong>and</strong> PV panels during the<br />
driving hours guarantees a charge sustaining strategy<br />
over the whole day. This con<strong>di</strong>tion is expressed as:<br />
∆SOCday ∫<br />
24h<br />
( φ)<br />
= dSOC(<br />
φ)<br />
dt =<br />
0<br />
= ∆SOC<br />
( φ)<br />
+ ∆SOC<br />
= 0<br />
d<br />
p<br />
(14)<br />
Assuming that the driving schedule, of duration hd<br />
hours, is composed of a sequence of ECE-EUDC<br />
cycles, eq. (14) can be satisfied by iteratively solving,<br />
over one cycle, the following nonlinear equation:<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 37
− ∆SOC<br />
p<br />
∆SOCECE ( φ ) =<br />
(15)<br />
N<br />
cycles<br />
Tab. I – Engine control strategies for HSV.<br />
A1 φ < 1 = 0<br />
A2 < 1<br />
h t φ < < 0<br />
P ICE<br />
d<br />
φ h < t < h<br />
φ ICE opt P P = d<br />
d<br />
B φ = 1 ICE opt P P = d h t < < 0<br />
C 1 < φ < φmax<br />
PICE = φPopt<br />
d h t < < 0<br />
where Ncycles is evaluated as function of each module<br />
duration Tcycle (h):<br />
hd<br />
N cycles = (16)<br />
T<br />
cycle<br />
The results obtained in previous papers show that<br />
relevant fuel savings, up to 45% for intermittent use<br />
in urban driving, can be obtained by a proper<br />
optimization of vehicle <strong>and</strong> powertrain components,<br />
<strong>and</strong> that this kind of vehicle is not far from economic<br />
feasibility, considering actual <strong>and</strong> expected trends in<br />
oil price <strong>and</strong> vehicle components (solar panels,<br />
batteries) [11][12][18].<br />
4. RESULTS<br />
The simulation results presented in this section are<br />
related to a prototype of solar hybrid vehicle with<br />
series structures that is being developed at the<br />
University of <strong>Salerno</strong>, within the EU supported<br />
Leonardo Program I05/B/P/PP-154181 “Energy<br />
Conversion Systems <strong>and</strong> Their Environmental<br />
Impact” (www.<strong>di</strong>mec.unisa.it/leonardo).<br />
The prototype is being developed starting from the<br />
Electric Vehicle Piaggio-Micro-Vett Porter (shown in<br />
Figure 2), whose main features concerning vehicle<br />
<strong>and</strong> electric motor are summarized in Tab. II. With<br />
the ad<strong>di</strong>tion of solar panels <strong>and</strong> electric generator,<br />
whose details also are given in Tab. II, the HSV is<br />
obtained.<br />
Figure 3 shows the driving cycle selected for the<br />
simulation tests, which is derived from the European<br />
Driving Cycle (ECE) <strong>and</strong> is representative of a<br />
generic urban route.<br />
The power contributions of electric generator (EG),<br />
solar panels (PV) <strong>and</strong> battery (B) to drive the HSV<br />
along the imposed route is shown in Figure 4, while<br />
Figure 5 shows a comparison of SOC history between<br />
HSV, pure Electric Vehicle (EV) <strong>and</strong> solar electric<br />
vehicle (SEV), the latter been derived from the EV by<br />
the ad<strong>di</strong>tion of solar panels to the base vehicle.<br />
Figure 4 evidences that since the variable φ is lower<br />
than 1, accor<strong>di</strong>ng to the imposed control strategy<br />
(Tab. I), the EG can be operated in an intermittent<br />
way at constant load <strong>and</strong> speed, correspon<strong>di</strong>ng to its<br />
highest efficiency (black line). Thus, in the former<br />
part of the transient, the drive power (blue line) is<br />
exclusively supplied by the batteries (red line) that<br />
experience a decrease of State of Charge (SOC), as<br />
shown in Figure 5. This trend is inverted around 650 s<br />
when the EG is switched on <strong>and</strong> powers both vehicle<br />
<strong>and</strong> battery in order to meet the charge sustaining<br />
strategy (see Figure 5). Of course, due to the<br />
constraint introduced by eq. (15), the final SOC<br />
<strong>di</strong>ffers from the initial value by a fraction of the<br />
amount of energy provided by the PV panels during<br />
parking hours.<br />
Figure 2 – The Micro-Vett Porter Electric Vehicle.<br />
Tab. II – Electric Vehicle Technical Data.<br />
Vehicle<br />
(EV, SEV, HEV)<br />
Piaggio Micro-Vett Porter<br />
Length 3.370 m<br />
Width 1.395 m<br />
Height 1.870 m<br />
Weight 1620 kg<br />
Drive ratio 1:4.875<br />
CX 0.4<br />
Electric Motor<br />
(EV, SEV, HSV)<br />
BRUSA MV 200 – 84 V<br />
Max speed 52 Km/h<br />
Continuous Power 9 KW<br />
Peak Power 15 KW<br />
Batteries<br />
(EV, SEV, HSV)<br />
14 Modules Pb-Gel<br />
Mass 226 Kg<br />
Capacity 130 Ah<br />
Photovoltaic Panels<br />
(SEV, HSV)<br />
Polycrystalline<br />
Surface 1.44 m 2<br />
Weight 60 kg<br />
Efficiency 0.13<br />
Electric Generator Lombar<strong>di</strong>ni (500 cc engine,<br />
(HSV) 3 phase induction machine)<br />
Max Power 15 kW<br />
Max Efficiency 25 % @ 9 KW<br />
Weight 100 kg<br />
It is worth noting that the occurrence of an initial<br />
<strong>di</strong>scharging process, followed by a recharging one,<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 38
esults in benefits for batteries losses since the lower<br />
is the SOC, the more efficient is the recharging phase.<br />
On the other h<strong>and</strong>, the SOC trajectories simulated for<br />
EV <strong>and</strong> SEV (see Figure 5) both show a decreasing<br />
trend. This is expected in EV because battery<br />
recharge is performed by connection to the grid. For<br />
the SEV the same recharging strategy must be<br />
adopted since the amount of energy provided by the<br />
PV panels mounted on the roof is relatively small.<br />
35<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
Reference vehicle speed [km/h]<br />
0<br />
0 200 400<br />
Time [s]<br />
600 800<br />
10<br />
5<br />
0<br />
Figure 3 – Selected driving cycle.<br />
HSV power KW<br />
-5<br />
drive<br />
gen<br />
sun<br />
-10 batt<br />
0 200 400<br />
Time [s]<br />
600 800<br />
Figure 4 – Power contributions simulated for the<br />
HSV over the selected driving cycle.<br />
0.76<br />
0.755<br />
0.75<br />
0.745<br />
0.74<br />
SOC [/]<br />
∆SOC<br />
−<br />
N<br />
cycles<br />
0.735<br />
0.73<br />
EV<br />
SEV<br />
HSV<br />
0.725<br />
0 200 400<br />
Time [s]<br />
600 800<br />
Figure 5 – Battery state of charge trajectories for<br />
the three simulated vehicles.<br />
Nevertheless, battery recharge in SEV is postponed<br />
with respect to EV due to the amount of energy<br />
provided by PV during parking hours. In the SEV<br />
p<br />
simulation this is taken into account by shifting up the<br />
initial SOC by a fraction of the energy stored during<br />
parking hours (Figure 5). This leads to a final SOC<br />
higher than the EV, which in turn results in increasing<br />
vehicle autonomy by about 30 % (125 against 95 km<br />
per battery cycle). Such a significant improvement<br />
in<strong>di</strong>cates the use of PV panels as range extender of<br />
electric vehicles as a high potential application of<br />
solar energy in the transportation field.<br />
2.4 Comparison with conventional vehicle equipped<br />
with ICE<br />
The achievement of a charge sustaining strategy with<br />
the HSV suggests the need for assessing fuel<br />
economy improvements <strong>and</strong> economical aspects<br />
related to the solar hybri<strong>di</strong>zation of conventional cars.<br />
For this purpose, in this section a comparative<br />
analysis is performed on the HSV presented before<br />
<strong>and</strong> the ICE-powered Porter commercialized by<br />
Piaggio (equipped with an S.I. engine 1.2 liters with a<br />
max power of 48 KW; overall vehicle weight is 1550<br />
kg).<br />
Figure 6 shows a comparison of engine speeds in case<br />
of hybrid <strong>and</strong> conventional vehicle, evidencing that in<br />
the latter case (solid line), the engine always operates<br />
in transient con<strong>di</strong>tions <strong>and</strong> partial loads, with higher<br />
values of specific fuel consumption <strong>and</strong> poor<br />
efficiency, as evidenced in Figure 7. On the other<br />
h<strong>and</strong>, as already shown in Figure 4, the hybrid vehicle<br />
ICE generator works only in the latter part of the<br />
transient, operating at constant speed (i.e. 3000 rpm)<br />
correspon<strong>di</strong>ng to its maximum efficiency (i.e. 32%).<br />
The <strong>di</strong>fferent behaviour of engine operation results in<br />
a significant improvement in fuel economy in case of<br />
HSV, as in<strong>di</strong>cated in Tab. III. For the selected driving<br />
cycle, the amount of fuel needed by the hybrid<br />
vehicle is 50 % less than that required by the ICEpowered<br />
vehicle.<br />
3500<br />
3000<br />
2500<br />
2000<br />
1500<br />
1000<br />
500<br />
HSV<br />
CV<br />
rpm [rev/min]<br />
0<br />
0 200 400<br />
Time [s]<br />
600 800<br />
Figure 6 – Comparison between HSV <strong>and</strong> CV<br />
ICE’s rpm over the imposed driving cycle.<br />
Tab. III also gives the pay-back in terms of years<br />
necessary to restore the ad<strong>di</strong>tional costs of the HSV<br />
with respect to the conventional vehicle. With the<br />
actual costs of fuel <strong>and</strong> PV the pay-back equals 7.7<br />
years, whereas assuming to double the fuel price <strong>and</strong><br />
to reduce by 75 % the PV cost, the pay-back reduces<br />
considerably, down to 2.4 years.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 39
It is worth mentioning here that other strategies are<br />
possible for HSV control, such as letting the ICE run<br />
during parking mode too: in that case, the engine can<br />
be used to restore battery charge by working always<br />
at its maximum efficiency.<br />
0.35<br />
0.3<br />
0.25<br />
0.2<br />
0.15<br />
0.1<br />
0.05<br />
HSV<br />
CV<br />
ICE efficiency [/]<br />
0<br />
0 200 400<br />
Time [s]<br />
600 800<br />
Figure 7 – Comparison between CV <strong>and</strong> HSV<br />
ICE’s Efficiency over the imposed driving cycle.<br />
Tab. III – Energetic <strong>and</strong> economical aspects<br />
associated with solar hybri<strong>di</strong>zation.<br />
Fuel consumption<br />
(g per cycle)<br />
HSV CV<br />
79 158<br />
Weight (kg) 1780 1550<br />
Pay-back<br />
(years, with actual costs)<br />
Pay-back<br />
(years, considering future<br />
cost trends)<br />
7.7 /<br />
2.4 /<br />
Driving hours per day 2 2<br />
Insolation (KWh/m 2 /day) 4.3017<br />
5. CONCLUSION<br />
A dynamic model for the simulation of a solar hybrid<br />
prototype based on the electrical vehicle Piaggio<br />
Micro-Vett Porter has been presented. The model<br />
describes the energy flows between photovoltaic<br />
panels, internal combustion engine (ICE), electric<br />
generator, electric motor <strong>and</strong> batteries, considering<br />
vehicle longitu<strong>di</strong>nal dynamics <strong>and</strong> the effect of<br />
control strategies. Vehicle weight is computed<br />
starting from the electrical vehicle weight,<br />
considering the effects of ad<strong>di</strong>tional components<br />
(ICE-generator, photovoltaic panels, etc.). The model<br />
also pre<strong>di</strong>cts the ad<strong>di</strong>tional costs with respect to<br />
conventional vehicle <strong>and</strong> the pay-back.<br />
The simulation performed along a urban driving cycle<br />
has shown that the hybrid vehicle can accomplish a<br />
charge sustaining strategy with intermittent use of<br />
ICE-generator at maximum efficiency. Comparison<br />
with conventional vehicle powered with ICE has<br />
evidenced a significant improvement in terms of fuel<br />
economy, close to 50 % in the selected driving cycle.<br />
Furthermore, the pay-back to restore the ad<strong>di</strong>tional<br />
costs of hybrid components is 7.7 years with actual<br />
costs of fuel <strong>and</strong> components while it decreases to 2.4<br />
years assuming to double the fuel price <strong>and</strong> to reduce<br />
the panels cost by 75%, in accordance with the actual<br />
<strong>and</strong> expected trends in costs <strong>and</strong> prices.<br />
6. REFERENCES<br />
[1] Hammad M., Khatib T. (1996), Energy<br />
Parameters of a <strong>Solar</strong> Car for Jordan, Energy<br />
Conversion Management, V.37, No.12.<br />
[2] Wellington R.P. (1996), Model <strong>Solar</strong> <strong>Vehicles</strong><br />
Provide Motivation for School Students, <strong>Solar</strong><br />
Energy Vol.58, N.1-3.<br />
[3] Saitoh, T.; Hisada, T.; Gomi, C.; Maeda, C.<br />
(1992), Improvement of urban air pollution via<br />
solar-assisted super energy efficient vehicle. 92<br />
ASME JSES KSES Int Sol Energy Conf. Publ by<br />
ASME, New York, NY, USA.p 571-577.<br />
[4] Sasaki K., Yokota M., Nagayoshi H., Kamisako<br />
K. (1997), Evaluation of an Electric Motor <strong>and</strong><br />
Gasoline Engine <strong>Hybrid</strong> Car Using <strong>Solar</strong> Cells,<br />
<strong>Solar</strong> Energy Material <strong>and</strong> <strong>Solar</strong> Cells (47), 1997.<br />
[5] Seal M.R. (1995), Viking 23 - zero emissions in<br />
the city, range <strong>and</strong> performance on the freeway.<br />
Northcon - Conference Record 1995. IEEE, RC-<br />
108.p 264-268.<br />
[6] Seal M.R., Campbell G. (1995), Ground-up<br />
hybrid vehicle program at the vehicle research<br />
institute. Electric <strong>and</strong> <strong>Hybrid</strong> <strong>Vehicles</strong> -<br />
Implementation of Technology SAE Special<br />
Publications n 1105 1995.SAE, Warrendale, PA,<br />
USA.p 59-65.<br />
[7] S.Letendre, R.Perez, Christy Herig, Vehicle<br />
Integrated PV: a Clean <strong>and</strong> Secure Fuel for<br />
<strong>Hybrid</strong> Electric <strong>Vehicles</strong>, Proc. of Annual<br />
Meeting of the American <strong>Solar</strong> Energy Society,<br />
June 21-26, 2003, Austin, TX.<br />
[8] Arsie I., Graziosi M., Pianese C., Rizzo G.,<br />
Sorrentino M. (2004), Optimization of<br />
Supervisory Control Strategy for Parallel <strong>Hybrid</strong><br />
Vehicle with Provisional Load Estimate, Proc. of<br />
AVEC04, Arhnem (NL), Aug.23-27, 2004.<br />
[9] Statistics for Road Transport, UK Government,<br />
http://www.statistics.gov.uk/CCI/nscl.asp?ID=81<br />
00.<br />
[10] http://ww.itee.uq.edu.au/~serl/UltraCommuter.ht<br />
ml.<br />
[11] Arsie I., Marotta M., Pianese C., Rizzo G.,<br />
Sorrentino M., Optimal Design of a <strong>Hybrid</strong><br />
Electric Car with <strong>Solar</strong> Cells, Proc. of 1st<br />
AUTOCOM Workshop on Preventive <strong>and</strong> Active<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 40
Safety Systems for Road <strong>Vehicles</strong>, Istanbul,<br />
Sept.19-21, 2005.<br />
[12] Arsie I., Rizzo G., Sorrentino M., Optimal<br />
Design <strong>and</strong> Dynamic Simulation of a <strong>Hybrid</strong><br />
<strong>Solar</strong> Vehicle, SAE paper 2006-01-2997.<br />
[13] Marion B. <strong>and</strong> Anderberg M., “PVWATTS – An<br />
online performance calculator for Grid-<br />
Connected PV Systems”, Proc.of the ASES <strong>Solar</strong><br />
2000 Conf., June 16-21, 2000, Ma<strong>di</strong>son, WI.<br />
[14] http://www.autosteel.org/articles/2001_au<strong>di</strong>_a2.<br />
htm<br />
[15] Arsie I., Flora R., Pianese C., Rizzo G., Serra G.,<br />
A Computer Code for S.I. Engine Control <strong>and</strong><br />
Powertrain Simulation. SAE 2000 Transactions -<br />
Journal of Engines, Vol. 109-3, SAE Paper 2000-<br />
01-0938, pp. 935-949.<br />
[16] Burch, S., Cuddy, M., Johnson, V., Markel, T.,<br />
Rausen, D., Sprik, S., <strong>and</strong> Wipke, K., 1999,<br />
"ADVISOR: Advanced Vehicle Simulator",<br />
available at: http://www.ctts.nrel.gov.<br />
[17] Leonardo Program I05/B/P/PP-154181 “Energy<br />
Conversion Systems <strong>and</strong> Their Environmental<br />
Impact”, http://www.<strong>di</strong>mec.unisa.it/leonardo.<br />
[18] Arsie I., Rizzo G., Sorrentino M., Optimal<br />
Design of a <strong>Hybrid</strong> <strong>Solar</strong> Vehicle, Proc. of<br />
AVEC’06, Taipei (TW), August 20-24, 2006.<br />
[19] I.Arsie, M.Cacciato, A.Consoli, G.Petrone,<br />
G.Rizzo, M.Sorrentino, G.Spagnuolo, “<strong>Hybrid</strong><br />
<strong>Vehicles</strong> <strong>and</strong> <strong>Solar</strong> Energy: a Possible<br />
Marriage?”, ICAT06, November 17, 2006,<br />
Istanbul.<br />
7. CONTACT<br />
Ivan Arsie (iarsie@unisa.it)<br />
Raffaele Di Martino (r<strong>di</strong>martino@unisa.it)<br />
Gianfranco Rizzo (grizzo@unisa.it)<br />
Marco Sorrentino (msorrentino@unisa.it)<br />
Tel. +39 089 964080 – Fax +39 089 964037<br />
Web www.<strong>di</strong>mec.unisa.it<br />
8. DEFINITIONS, ACRONYMS,<br />
ABBREVIATIONS<br />
Es,p: <strong>Solar</strong> energy stored during parking hours (kWh)<br />
Es,d: <strong>Solar</strong> energy stored during driving hours (kWh)<br />
ηp: PV efficiency<br />
ΑPV: PV surface (m 2 )<br />
wICE: ICE weight to power ratio (kg/kW)<br />
wgear: Gearbox weight to power ratio (kg/kW)<br />
wEM: Electric motor weight to power ratio (kg/kW)<br />
wEG: Electric generator weight to power ratio (kg/kW)<br />
wB,u: Single battery module weight (kg/kW)<br />
wPV: PV specific weight (kg/m 2 )<br />
PEG: Electric generator power for HSV<br />
ηEG: Electric generator efficiency<br />
cICE: ICE cost to power ratio (Eur/kW)<br />
cEG: Electric generator cost to power ratio (Eur/kW)<br />
cPV: PV specific cost (Eur/m 2 )<br />
cEM: Electric motor cost to power ratio (Eur/kW)<br />
cB: Single battery module cost (Eur)<br />
cf: fuel unit cost (Eur/kg)<br />
nD: number of days per year in the pay-back analysis<br />
∆SOCday: state of charge variation over the whole day<br />
∆SOCd: state of charge variation in driving phases<br />
∆SOCp: state of charge variation in parking phases<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 41
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 42
A model of mismatched photovoltaic fields for simulating hybrid solar vehicles<br />
Abstract – A numerical model of photovoltaic fields that allows<br />
simulating both uniform <strong>and</strong> mismatched operating con<strong>di</strong>tions<br />
is introduced in this paper. It allows the simulation of a<br />
photovoltaic generator whose subsections, e.g. cells, groups of<br />
cells, panels or group of panels, work under <strong>di</strong>fferent solar<br />
irra<strong>di</strong>ation values <strong>and</strong>/or <strong>di</strong>fferent temperature. Furthermore,<br />
<strong>di</strong>fferent nominal characteristics, rated power, production<br />
technology, shape <strong>and</strong> area can be accounted for any<br />
subsections of the photovoltaic generator. The proposed model<br />
is reliable <strong>and</strong> results into a non linear system of equations that<br />
requires a moderate computational burdensome, both in terms<br />
of memory use <strong>and</strong> processor speed. Numeric simulations<br />
confirm the usefulness of the proposed approach in automotive<br />
applications, especially in solar hybrid vehicles, in order to<br />
design a proper electronic controller ensuring the extraction of<br />
the maximum power from the photovoltaic generator.<br />
I. INTRODUCTION<br />
Renewable energy sources are gaining more <strong>and</strong> more<br />
interest in recent years due to the exploitation of oilfields <strong>and</strong><br />
to political crises in some strategic areas of the world.<br />
Among them, photovoltaic (PV) sources have found new<br />
applications, e.g. solar hybrid vehicles. They work with<br />
greatly varying solar irra<strong>di</strong>ation levels due to the movement<br />
<strong>and</strong>, especially if the solar cells are not placed only on the<br />
roof, <strong>di</strong>fferent subsections of the PV generator may receive<br />
<strong>di</strong>fferent sun irra<strong>di</strong>ance levels.<br />
In any case, it is m<strong>and</strong>atory to match the PV source with the<br />
load/battery in order to draw the maximum power at the<br />
current solar irra<strong>di</strong>ance level. To this regard, a switching dcdc<br />
converter controlled by means of a Maximum Power<br />
Point Tracking (MPPT) strategy is suitable to ensure the<br />
source-load matching by properly changing the operating<br />
voltage at the PV array terminals in function of the actual<br />
weather con<strong>di</strong>tions. Any efficient MPPT technique must be<br />
able to detect the voltage value correspon<strong>di</strong>ng to the<br />
maximum power that can be delivered by the PV source.<br />
In literature, many MPPT strategies have been proposed, the<br />
greatest part of them being derived by the basic Perturb <strong>and</strong><br />
Observe (P&O) <strong>and</strong> Incremental Conductance (IC)<br />
approaches. Both P&O <strong>and</strong> IC strategies, if properly<br />
designed, correctly work in presence of a uniform irra<strong>di</strong>ance<br />
of the PV array, since they are able, although by means of<br />
<strong>di</strong>fferent processes, to detect the unique peak of the power<br />
vs. voltage characteristic of the PV array. Unfortunately, in<br />
automotive applications, the PV field does not receive a<br />
G.Petrone*, G.Spagnuolo*, M.Vitelli°<br />
*DIIIE, <strong>Università</strong> <strong>di</strong> <strong>Salerno</strong><br />
Via Ponte Don Melillo, Fisciano (SA), Italy<br />
gpetrone@unisa.it, spanish@ieee.org<br />
°DII, Seconda <strong>Università</strong> <strong>di</strong> Napoli<br />
Real Casa dell’Annunziata, Aversa (CE), Italy<br />
vitelli@unina.it<br />
uniform irra<strong>di</strong>ation <strong>and</strong>/or not all its parts (panels as well as<br />
single cells) work at the same temperature, so that<br />
mismatches among <strong>di</strong>fferent parts of the array may arise.<br />
Such a situation has been evidenced in literature <strong>and</strong> the<br />
detrimental effect due to a panel of a PV array working under<br />
an irra<strong>di</strong>ation level or at a temperature, which is sensibly<br />
<strong>di</strong>fferent than that characterising the other panels has been<br />
experimentally investigated.<br />
Mismatching con<strong>di</strong>tions are more likely to occur in<br />
automotive applications than in stationary ones. For example,<br />
parts of the array may be shaded by other parts of the vehicle<br />
when the sun is at low angle <strong>and</strong>, moreover, unpre<strong>di</strong>ctable<br />
sha<strong>di</strong>ng takes place when the vehicle passes under the<br />
shadows of buil<strong>di</strong>ngs, trees, advertising panels. Even in<br />
automotive applications characterized by a relatively small<br />
duty cycle in the use of the vehicle, mismatching may play a<br />
strong role on battery charging during the long parking time.<br />
In such cases the shadows produced by objects surroun<strong>di</strong>ngs<br />
the car can give rise to a marked waste of available solar<br />
energy.<br />
To relieve the power drop caused by a mismatch, a bypass<br />
<strong>di</strong>ode is used in anti-parallel with each PV basic unit, e.g. a<br />
panel. A blocking <strong>di</strong>ode is placed in series with each totem<br />
of PV basic units connected in series. This precaution<br />
increases the plant cost, but avoids that a basic PV unit or a<br />
series of them absorbs the current produced by others.<br />
Whenever a mismatch occurs, both P&O <strong>and</strong> IC based<br />
MPPT techniques have a high probability to fail the MPPT<br />
goal. Indeed, the power vs. voltage characteristic of a PV<br />
field under a uniform solar irra<strong>di</strong>ation exhibits a unique<br />
maximum point that is easily tracked by st<strong>and</strong>ard MPPT<br />
techniques. Unfortunately, mismatches deeply affect the<br />
shape of the PV characteristic, which may exhibit more than<br />
one peak, with one absolute maximum point <strong>and</strong> one or more<br />
relative points of maximum power. In this case, st<strong>and</strong>ard<br />
MPPT techniques are likely deceived <strong>and</strong> consequently track<br />
a point where dP/dv=0, but that is not the maximum power<br />
point.<br />
In order to design a MPPT strategy able to perform a<br />
“global” tracking of the true PV array voltage associated to<br />
the maximum power, without being trapped in local maxima,<br />
it is of fundamental importance the realization of an accurate<br />
numerical model of the PV field. It must be able to simulate<br />
the PV basic units mismatching in a reliable <strong>and</strong> fast manner,<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 43
also accounting for the behaviour of real bypass <strong>and</strong> blocking<br />
<strong>di</strong>odes.<br />
In this paper a model with these characteristics is introduced:<br />
features <strong>and</strong> drawbacks are illustrated by means of<br />
simulations carried out in Matlab <strong>and</strong> PSIM environments.<br />
The paper is organized as follows: Section II shows the<br />
details of the proposed model <strong>and</strong> puts in evidence its<br />
features. Section III shows the results of some application<br />
examples <strong>and</strong> Section IV is devoted to conclusions <strong>and</strong> hints<br />
for a future work.<br />
II. THE MODEL<br />
In fig.1 the usual circuit model of a photovoltaic (PV) panel<br />
is shown.<br />
Fig.1 Circuit model of a PV panel inclu<strong>di</strong>ng the bypass <strong>di</strong>ode Db.<br />
Such a model recurs in literature very often (e.g. in []). It<br />
includes the light induced current generator Iph <strong>and</strong> series<br />
<strong>and</strong> shunt resistances Rs <strong>and</strong> Rh respectively; Db is the<br />
bypass <strong>di</strong>ode. We suppose, without loss of generality, that<br />
one bypass <strong>di</strong>ode is placed in antiparallel with the whole<br />
panel.<br />
The relation between the PV generator current I <strong>and</strong> voltage<br />
V is evaluated by solving the following system of non linear<br />
equations:<br />
I<br />
I<br />
Vd<br />
⎛ ⎞<br />
⎜ Vt<br />
, d = I e −1⎟<br />
sat,<br />
d⎜<br />
⎟<br />
⎝ ⎠<br />
d (1)<br />
⎛<br />
⎜e<br />
⎜<br />
⎝<br />
⎞<br />
−1⎟<br />
⎟<br />
⎠<br />
V<br />
−<br />
Vt<br />
, db<br />
db = Isat,<br />
db<br />
(2)<br />
I = I + I − I − I<br />
(3)<br />
d<br />
db<br />
ph<br />
s<br />
d<br />
s<br />
h<br />
s<br />
( I I )<br />
V V + R ⋅I<br />
= V + R ⋅ −<br />
I<br />
= (4)<br />
V<br />
V + R ⋅<br />
( I − I )<br />
d<br />
s db<br />
h = =<br />
(5)<br />
R h R h<br />
It has been obtained by using Kirchhoff voltage <strong>and</strong> current<br />
laws (3) <strong>and</strong> (4), linear characteristic equations for shunt <strong>and</strong><br />
series resistors (4) <strong>and</strong> (5), <strong>and</strong> non linear equations for the<br />
<strong>di</strong>ode D included in the model of the panel (1), <strong>and</strong> for the<br />
db<br />
bypass <strong>di</strong>ode Db (2). In (1) Vt,d=ηd⋅VT,d <strong>and</strong> in (2)<br />
Vt,db=ηdb⋅VT,db, Vt,d <strong>and</strong> Vt,db are expressed as the product of<br />
the <strong>di</strong>ode ideality factor <strong>and</strong> the thermal voltage. The latter,<br />
as well as the two saturation currents Isat,d <strong>and</strong> Isat,db, depend<br />
on temperature T only, whilst the light induced current Iph<br />
depends on the irra<strong>di</strong>ance level S <strong>and</strong> on the array<br />
temperature T [1].<br />
The system of equations (1)-(5) clearly shows that the PV<br />
array current I is a nonlinear <strong>and</strong> implicit function of the PV<br />
array voltage V, of the irra<strong>di</strong>ance level S <strong>and</strong> of the<br />
temperature T. Nevertheless, such a non linear system can be<br />
symbolically solved in one of the symbolic calculation<br />
environments, such as Matlab <strong>and</strong> Mathematica, actually<br />
available. In this way, a non linear relationship between the<br />
current I <strong>and</strong> the voltage V at the basic PV unit terminals can<br />
be obtained. For space reasons such relationship is reported<br />
in (6), at the end of the paper. It makes use of the LambertW<br />
function of the term θ whose value depends on the terminal<br />
voltage V <strong>and</strong> is reported in (7).<br />
It is well known [3] that the LambertW function of the<br />
variable θ, herein in<strong>di</strong>cated as LambertW(θ), is a non linear<br />
function of θ <strong>and</strong> it is the inverse function of:<br />
( )<br />
θ<br />
f θ = θ⋅<br />
e<br />
(8)<br />
Note that the use of the LambertW function allows the<br />
apparently explicit calculation of the array current as a non<br />
linear function of the terminal voltage. The value of the<br />
Lambert function, for an assigned value of the independent<br />
variable θ, is efficiently provided in simulation environments<br />
such as Matlab <strong>and</strong> Mathematica.<br />
Expression (6), together with well known LambertW<br />
function properties, allow to calculate the first derivative of<br />
the panel’s current with respect to the terminal voltage, again<br />
in apparently explicit form. In (9) it has been reported the<br />
property expressing the derivative of the LambertW(θ)<br />
function with respect to θ, <strong>and</strong> in (10) the expression of the<br />
derivative of I with respect to V at the panel’s terminals is<br />
given (see the end of the paper). In this way, the <strong>di</strong>fferential<br />
conductance of the panel is explicitly expressed as function<br />
of the panel’s voltage V only, by means of a non linear<br />
function.<br />
Thus, in this way, both the PV current <strong>and</strong> its derivative with<br />
respect to the PV voltage have been expressed in closed form<br />
as functions of the sole voltage.<br />
This greatly helps in formalizing the non linear algebraic<br />
system that describes a PV field composed by an arbitrary<br />
number of panels ,which can be connected both in series <strong>and</strong><br />
in parallel.<br />
In order to explain this concept, let us refer to a string of PV<br />
panels connected in series. Fig.2 shows the string of N<br />
series-connected panels <strong>and</strong> the blocking <strong>di</strong>ode that avoids<br />
current backflows.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 44
Fig.2 String of N PV panels connected in series <strong>and</strong> inclu<strong>di</strong>ng the blocking<br />
<strong>di</strong>ode.<br />
In order to model this series, it is possible to build up a<br />
system of (N+1) equations in the same number of unknowns<br />
{V1,V2,...,Vk,...,VN-1,VN,V<strong>di</strong>ode}. It is enough to write one<br />
Kirchhoff voltage law <strong>and</strong> N Kirchhoff current laws. The<br />
topological constraints are formalized in (11) at the end of<br />
the paper; they can be matched with the N equations of the<br />
panels, expressed as in (6) in terms of Ik=Ik(Vk), k=1,2,...,N,<br />
<strong>and</strong> with the characteristic equation of the blocking <strong>di</strong>ode<br />
expressed in the form (1), <strong>and</strong> taking into account the<br />
dependency of such a characteristic equation from the<br />
physical parameters of the real <strong>di</strong>ode used. The non linear<br />
system (11) includes N non linear equations <strong>and</strong> one linear<br />
equation, the first one, in which the terminal voltage V, that<br />
is assumed to be a known term, appears . Each non linear<br />
equation includes only two of the (N+1) unknowns, <strong>and</strong> the<br />
first one is always V1. This choice has been made to simplify<br />
the expression of the Jacobian matrix needed to solve the non<br />
linear system by means of, for example, the Newton Raphson<br />
method.<br />
Thanks to (10) it is possible to obtain each term of the<br />
Jacobian matrix J as a function of the unknowns. Moreover,<br />
the structure of the system has been properly chosen in order<br />
to simplify the structure of the Jacobian matrix that, as it is<br />
well known, needs to be inverted when using Newton<br />
Raphson iterative methods. The Jacobian matrix structure is<br />
reported in (12) which puts in evidence that it is sparse <strong>and</strong><br />
with a pattern which is characteristic of doubly bordered <strong>and</strong><br />
<strong>di</strong>agonal square matrices [2]. Moreover, the first row is<br />
composed by (N+1) constants, while all the other rows<br />
require the evaluation of dI1/dV1 <strong>and</strong> the calculation of just<br />
another derivative. As a whole, the evaluation of the system<br />
(11) requires N times the use of the equation (6) <strong>and</strong> one<br />
time the (1); the calculation of the Jacobian matrix requires<br />
N evaluations of (10) <strong>and</strong> one evaluation of (13).<br />
Such features are useful both in terms of memory<br />
requirements during the simulation <strong>and</strong> of computation time.<br />
In Section III the features of the method are described by<br />
means of a numeric example.<br />
III. SIMULATION RESULTS<br />
Simulations have been conducted by considering Kyocera<br />
KC120 PV panels, characterized by 36 series connected<br />
cells, each one of area 0.0225 m 2 , Rs=0.006 Ω, Rh=10 4 Ω.<br />
A string with two PV panels connected in series, <strong>and</strong> with<br />
the blocking <strong>di</strong>ode has been simulated. In this case the order<br />
of the system is 3. The panels have been considered identical<br />
in terms of manufacturing parameters <strong>and</strong> working<br />
temperature (T=320K).<br />
On the other h<strong>and</strong>, their irra<strong>di</strong>ation level has been considered<br />
very <strong>di</strong>fferent, namely S=1000 W/m 2 for the first panel <strong>and</strong><br />
S=100 W/m 2 for the second one.<br />
The whole simulation has been conducted in Matlab<br />
environment; it required 45.3 s (on an Intel Centrino 2.0 GHz<br />
platform) in order to calculate 100 linearly spaced points of<br />
the power-voltage characteristic of the PV array. The<br />
samples of the current in the series <strong>and</strong> of the voltage<br />
<strong>di</strong>stribution over the three devices have been also stored<br />
during simulation. The curves are reported in figs.3 <strong>and</strong> 4.<br />
They put in evidence the effect of the panel that receives the<br />
lower irra<strong>di</strong>ance level in terms of string current drop at high<br />
voltage values.<br />
It is worth noting that the curve of fig.3, obtained under<br />
mismatching con<strong>di</strong>tions of the PV string, exhibits two<br />
maxima at two <strong>di</strong>fferent voltage levels, with that one<br />
occurring at about 44 V being characterised by a consistently<br />
lower value of the power with respect to the other one placed<br />
at about 18 V. This occurrence can compromise the energy<br />
conversion operated by the switching converter connected at<br />
the string terminals <strong>and</strong> responsible for the MPPT. This can<br />
be understood by comparing plots of fig.3, representing the<br />
mismatched string, with that one of fig.5, obtained by<br />
imposing a unique irra<strong>di</strong>ance level S=1000 W/m 2 for both<br />
the panels. If the MPPT controller acts so that the string<br />
works at about 40 V under uniform irra<strong>di</strong>ance, it ensures that<br />
the maximum power – about 260 W – is converted. If a<br />
sudden irra<strong>di</strong>ance drop (from S=1000 W/m 2 to S=100 W/m 2 )<br />
occurs on one panel <strong>and</strong> the MPPT algorithm is not able to<br />
perform a “global search” of the new maximum power point,<br />
the relative maximum placed at about 40 V (see fig.3) is the<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 45
likely new operating point. This means that the MPPT<br />
controller is not able to track the real maximum power point<br />
<strong>and</strong> that about 90 W (the <strong>di</strong>fference between the maximum<br />
power of the best operating point at about 18 V <strong>and</strong> the<br />
power of an operating point placed at about 44 V) are wasted<br />
due to MPPT algorithm limit.<br />
Such considerations have been verified by means of a PSIM<br />
simulation of the PV field controlled by means of boost<br />
switching converter that performs the MPPT function <strong>and</strong><br />
matches the PV field with a 48V battery (see fig.6). The<br />
layout puts in evidence two dynamic link libraries that<br />
implement the PV field (left) <strong>and</strong> the P&O based MPPT<br />
controller (bottom). It has been simulated a sun irra<strong>di</strong>ance<br />
drop involving one of the two panels of the array: the steep<br />
transition between the characteristic of fig.5 <strong>and</strong> that one of<br />
fig.3 occurs at t=0.03s (see fig.7). The P&O controller tracks<br />
the lower maximum because the voltage at which it occurs<br />
(see fig.8) is close to the voltage correspon<strong>di</strong>ng to the unique<br />
maximum of the characteristic depicted in fig.5. Fig.7 also<br />
put in evidence the three-points behaviour at both steady<br />
states: this characterizes the hill climbing of the two<br />
maximum power points tracked at the two <strong>di</strong>fferent<br />
con<strong>di</strong>tions. This result is confirmed by the boost converter<br />
duty cycle variation shown in fig.9.<br />
In conclusion, the model illustrated in this paper might be of<br />
great help in developing an improved MPPT algorithm that is<br />
robust with respect to this kind of con<strong>di</strong>tions, since it allows<br />
to test the MPPT performances with respect to <strong>di</strong>fferent<br />
shapes of the power-voltage characteristic of the PV<br />
generator.<br />
IV. CONCLUSIONS AND FUTURE WORK<br />
In this paper a non linear model of mismatched photovoltaic<br />
fields is introduced. It allows to simulate heterogeneous<br />
arrays, with subsections (cells, groups of cells, panels or<br />
groups of panels) characterized by <strong>di</strong>fferent irra<strong>di</strong>ation<br />
levels, temperatures, semiconductor materials, areas,<br />
operating parameters <strong>and</strong> so on. The model also allows to<br />
take into account manufacturing tolerances <strong>and</strong> drifts<br />
ascribable to aging effects.<br />
Further work is in progress in order to use the simulator in<br />
order to develop <strong>and</strong> test a maximum power point tracking<br />
strategy able to ensure an efficient power conversion even if<br />
the photovoltaic field works in mismatched con<strong>di</strong>tions.<br />
REFERENCES<br />
[1] S. Liu, R. A. Dougal: ”Dynamic multiphysics model<br />
for solar array”, IEEE Trans. On Energy Conversion, Vol.<br />
17, No. 2, June 2002, pp. 285-294.<br />
[2] William H. Press, Numerical Recipes in C, The Art<br />
of Scientific Computing, Second E<strong>di</strong>tion, Cambridge<br />
University Press, 2002.<br />
[3] http://mathworld.wolfram.com/LambertW-<br />
Function.html<br />
120<br />
W<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
0 5 10 15 20 25<br />
V<br />
30 35 40 45 50<br />
Fig 3. Power [W] vs. voltage [V] characteristic of the simulated mismatched<br />
PV field.<br />
7<br />
A<br />
6<br />
5<br />
4<br />
3<br />
2<br />
1<br />
0<br />
-2 0 2 4 6 8 10 12 14 16 18 20 22 24 26<br />
V<br />
Fig.4 Current [A] vs. voltage [V] characteristic of the three devices in the<br />
simulated string. Continuous line = blocking <strong>di</strong>ode curve, dashed line =<br />
curve of the panel with irra<strong>di</strong>ation S=100 W/m 2 , dash-dotted line = curve of<br />
the panel with irra<strong>di</strong>ation S=1000 W/m 2 .<br />
300<br />
W<br />
250<br />
200<br />
150<br />
100<br />
50<br />
0<br />
0 5 10 15 20 25 30 35 40 45 50<br />
V<br />
Fig 5. Power vs. voltage characteristic of the simulated matched PV field.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 46
I =<br />
[ R ⋅(<br />
I + I ) − V]<br />
Figure 7. PV field output power.<br />
Figure 8. PV field voltage.<br />
Figure 6. PSIM layout for the simulation of the MPPT controller.<br />
⎛<br />
⋅⎜<br />
e<br />
⎜<br />
⎝<br />
V<br />
−<br />
h ph sat,<br />
d<br />
Vt<br />
, db<br />
+ Isat,<br />
db<br />
( R h + R s )<br />
⎞<br />
⎟ Vt<br />
−1<br />
−<br />
⎟ R<br />
⎠<br />
, d<br />
s<br />
⋅ LambertW<br />
( θ)<br />
Figure 9. Duty cycle during transient.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 47<br />
(6)
( R // R ) ⋅<br />
⎡ Rh<br />
⋅Rs<br />
⎢<br />
⋅(<br />
I + I )<br />
⎢⎣<br />
Vt<br />
, d⋅(<br />
Rh<br />
+ Rs<br />
) ⎥⎦<br />
h s Isat,<br />
d ⋅e<br />
θ =<br />
(7)<br />
d<br />
LambertW<br />
dθ<br />
dI<br />
dV<br />
= −<br />
1<br />
( θ)<br />
=<br />
V<br />
t,<br />
d<br />
ph<br />
sat , d + R h⋅V<br />
⎤<br />
⎥<br />
1<br />
( θ)<br />
[ 1+<br />
LambertW(<br />
θ)<br />
] ⋅e<br />
I<br />
−<br />
sat,<br />
db<br />
⋅e<br />
−<br />
V<br />
Vt<br />
, db<br />
( R + R ) V R ⋅(<br />
R + R )<br />
h<br />
s<br />
t,<br />
db<br />
⎧V1<br />
+ V2<br />
+ K+<br />
Vk<br />
+ K+<br />
V<br />
⎪<br />
⎪<br />
I1<br />
⎪<br />
I1<br />
1 3<br />
⎪<br />
⎪<br />
K<br />
⎨<br />
I1<br />
1 k<br />
⎪<br />
⎪<br />
K<br />
⎪<br />
I<br />
⎪<br />
I1<br />
⎪<br />
⎪⎩<br />
I1<br />
1 <strong>di</strong>ode<br />
⎛ 1<br />
⎜ ∂I1<br />
⎜<br />
⎜ ∂V1<br />
⎜ ∂I1<br />
⎜ ∂V1<br />
⎜<br />
⎜ M<br />
⎜ ∂I1<br />
J =<br />
⎜ ∂V1<br />
⎜<br />
⎜<br />
M<br />
⎜ ∂I1<br />
⎜ ∂V1<br />
⎜<br />
⎜<br />
∂I1<br />
⎜ ∂V1<br />
⎜ ∂I1<br />
⎜<br />
⎝ ∂V1<br />
∂I<br />
∂V<br />
1<br />
∂I<br />
2 −<br />
∂V<br />
N−1<br />
( V1<br />
) − I2<br />
( V2<br />
)<br />
( V ) − I ( V )<br />
( V ) − I ( V )<br />
−<br />
+ V<br />
= 0<br />
= 0<br />
= 0<br />
1(<br />
V1<br />
) − I N−1(<br />
VN−1<br />
)<br />
( V1<br />
) − I N ( VN<br />
) = 0<br />
( V ) − I ( V )<br />
2<br />
1<br />
∂I3<br />
−<br />
∂V<br />
0<br />
V<strong>di</strong>ode<br />
I<br />
<strong>di</strong>ode sat,<br />
<strong>di</strong>ode Vt<br />
, <strong>di</strong>ode<br />
= − ⋅e<br />
<strong>di</strong>ode<br />
V<br />
t,<br />
<strong>di</strong>ode<br />
3<br />
3<br />
k<br />
...<br />
O<br />
<strong>di</strong>ode<br />
N<br />
s<br />
+ V<br />
= 0<br />
= 0<br />
1<br />
∂I<br />
k −<br />
∂V<br />
k<br />
R<br />
=<br />
LambertW(<br />
θ)<br />
[ 1+<br />
LambertW(<br />
θ)<br />
] ⋅θ<br />
LambertW (9)<br />
h<br />
h<br />
<strong>di</strong>ode<br />
...<br />
O<br />
s<br />
− V = 0<br />
⋅ LambertW<br />
1<br />
∂I<br />
−<br />
∂V<br />
N−1<br />
N−1<br />
1<br />
0<br />
( θ)<br />
∂I<br />
−<br />
∂V<br />
N<br />
N<br />
1<br />
∂I<br />
−<br />
∂V<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 48<br />
<strong>di</strong>ode<br />
<strong>di</strong>ode<br />
⎞<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎟<br />
⎠<br />
(10)<br />
(11)<br />
(12)<br />
(13)
THE PROFITABLENESS OF HYBRID SOLAR VEHICLES (HSV)<br />
Ion V. Ion, Ion C. Ionita, Daniela Negoita, Spiru Paraschiv<br />
„Lower Danube” University of Galati – Romania<br />
Thermodynamics <strong>and</strong> Heat Engines Department<br />
Abstract. Being conscious that nowadays in the starting stage the competition between<br />
classical car, powered by combustion engine <strong>and</strong> the HSV can live <strong>and</strong> develop only<br />
with an ad<strong>di</strong>tional financial support, the authors focused their attention on<br />
mathematical expression of this support. They found the factors affecting the value of<br />
this support <strong>and</strong> the con<strong>di</strong>tions making HSV profitable. The analysis is based on the<br />
compared cost to quality analysis, developed in the last 10 years.<br />
Keywords: Compared cost-to-quality analysis<br />
List of the used symbols<br />
Latin letters:<br />
ICE<br />
C -the total cost of a classical car, powered by<br />
P<br />
internal combustion engine, [€ / ICE car]<br />
HSV<br />
CP -the total cost of a HSV, [€ / HSV]<br />
ICE<br />
CS -the cost of the transport service in the case<br />
ICE, [€/ km ICE]<br />
HSV<br />
CS -the cost of the transport service with HSV,<br />
[€/ km HSV]<br />
ICE<br />
C -the investment cost of the ICE transport<br />
( S )<br />
I<br />
service, [€ / km ICE]<br />
ICE<br />
C -the consumption cost of the ICE transport<br />
( S )<br />
C<br />
service, [€ / km ICE]<br />
ICE<br />
C -the operation-maintenance cost of the<br />
( S )<br />
OM<br />
ICE transport service, [€ / km ICE]<br />
HSV<br />
C -the investment cost of the HSV transport<br />
( S )<br />
I<br />
service, [€ / km HSV]<br />
HSV<br />
C -the consumption cost of the HSV<br />
( S )<br />
C<br />
transport service, [€ / km HSV]<br />
HSV ( S )<br />
C -the operation-maintenance cost of the HSV<br />
OM<br />
transport service, [€ / km HSV]<br />
ICE<br />
C -the investment cost of the ICE car, [€/car ICE]<br />
( P )<br />
( ICE<br />
P )<br />
I<br />
C -the consumption cost of the ICE car, [€/car<br />
C<br />
ICE]<br />
ICE<br />
C -the operation-maintenance cost of the ICE car,<br />
( P )<br />
OM<br />
[€/car ICE]<br />
HSV<br />
C -the investment cost of the HSV, [€/ HSV]<br />
( P )<br />
( HSV<br />
P )<br />
( HSV<br />
P )<br />
I<br />
C -the consumption cost of the HSV, [€ / HSV]<br />
C<br />
C -the operation-maintenance cost of the HSV,<br />
OM<br />
[€/ HSV]<br />
HSV<br />
sOM -the operation-maintenance ratio of HSV car service,<br />
(eq. 8);<br />
ICE<br />
sOM -the operation-maintenance ratio of ICE car service,<br />
(eq. 7)<br />
ICE<br />
f -the unitary fuel consumption of ICE, [l/100km ICE]<br />
ICE<br />
c f -the unitary fuel cost, [€ / l fuel]<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 49
HSV<br />
k f -the fuel reduction ratio of HSV, (eq. 10);<br />
ICE<br />
pOM -the operation-maintenance ratio of ICE car<br />
product, (eq. 11);<br />
p -the operation-maintenance ratio of HSV<br />
HSV<br />
OM<br />
product, (eq. 12);<br />
S - the state unitary subsidy of HSV program,<br />
100 km<br />
HSV<br />
[€/ 100 km]<br />
Greek letters:<br />
ICE<br />
τ -the total life cycle of an ICE car, [km ICE /<br />
ICE car]<br />
HSV<br />
τ -the total life cycle of a HSV, [km HSV / HSV<br />
car]<br />
Subscripts:<br />
I-investment<br />
C- consumption<br />
P – product<br />
S – service<br />
OM - operation-maintenance<br />
f – fuel<br />
HSV – hybrid solar vehicle<br />
Superscripts:<br />
ICE – internal combustion engine<br />
HSV – hybrid solar vehicle<br />
1. INTRODUCTION<br />
The purpose of this paper is to analyze<br />
mathematically the con<strong>di</strong>tions when HSV could be<br />
profitable. Starting on this way, we know that<br />
presently the classical cars are cheaper than HSV.<br />
This reality can be changed not so late in the future<br />
because of some tendencies we see:<br />
1) The classical cars pollution is increasing<br />
permanently, due to raising number of vehicles, in<br />
spite of their lowering in<strong>di</strong>vidual pollution;<br />
2) The solar cell panels are permanently perfectible<br />
<strong>and</strong> their efficacy is continuously increasing while<br />
their cost is lower <strong>and</strong> lower;<br />
3) The unitary cost of organic fuel is presently<br />
increasing exponentially. Being conscious that<br />
nowadays in the starting stage the competition<br />
between classical car, powered by combustion<br />
engine <strong>and</strong> the HSV can live <strong>and</strong> develop only with<br />
an ad<strong>di</strong>tional financial support, the authors focused<br />
their attention on mathematical expression of this<br />
support. They found the factors affecting the value of<br />
this support <strong>and</strong> the con<strong>di</strong>tions making HSV<br />
profitable.<br />
The analysis is based on the compared cost-to-<br />
quality analysis, developed in the last 10 years [6 –<br />
18]. To obtain the expression of the necessary<br />
subsidy, the authors considered two evident <strong>di</strong>fferent<br />
cases:<br />
a) the case of a classical car, powered by<br />
combustion engine (symbols with superscript ICE);<br />
b) the case of a HSV powered both by combustion<br />
engine <strong>and</strong> by photo-voltaic (PV) panels (symbols<br />
with superscript HSV).<br />
2. CHOOSING THE NECESSARY COST-<br />
TO QUALITY RATIO<br />
As the compared cost- to- quality analysis needs, when<br />
starting the evaluation it is necessary to choose an<br />
adequate cost-to quality ratio. There are two possible<br />
variants:<br />
a. The production variant, where we have to calculate in<br />
terms of Euro/car;<br />
b. The service variant, accountable in terms of Euro/100<br />
covered kilometers.<br />
The authors considered the second variant option (b) to be<br />
more appropriate because it expresses better the service<br />
the car does, taking into consideration that the car is used<br />
more or less during its life cycle span.<br />
3. THE QUALITY PARAMETERS OF THE<br />
CONSIDERED CARS<br />
For each car, classical or hybrid one, there are 31 <strong>di</strong>fferent<br />
quality parameters: QP 01–Accessibility; QP 02–<br />
Adaptability; QP 03–Availability; QP 04–Cleanliness;<br />
QP 05–Cre<strong>di</strong>bility; QP 06–Durability; QP 07–<br />
Environmental Protection; QP 08–Fuel Consumption;<br />
QP 09–Functional Engine Parameters; QP 10–<br />
Inflammability; QP 11–Lighting Parameters; QP 12–Look;<br />
QP 13–Maintainability; QP 14–Parking Capacity; QP 15–<br />
Productivity; QP 16–Promptitude; QP 17–Protection;<br />
QP 18-PV Panel Parameters; QP 19-Reliability; QP 20–<br />
Safety; QP 21–Size; QP 22–Style; QP 23–Susceptibility;<br />
QP 24–Pneumatic Tires Parameters; QP 25–Toxicity;<br />
QP 26–Transportability; QP 27–Transport Capacity;<br />
QP 28–Vulnerability; QP 29–Watching capacity;<br />
QP 30–Weight; QP 31–Workings.<br />
When considering the transport service made by these<br />
cars, we have at least another 15 parameters: QS 01–<br />
Accessibility; QS 02–Accuracy; QS 03–Comfort; QS 04–<br />
Competence; QS 05–Confidence; QS 06–Cre<strong>di</strong>bility; QS<br />
07–Efficacy; QS 08–Efficiency; QS 09–Feedback speed;<br />
QS 10–Formalism; QS 11–Honesty; QS 12–Proficiency;<br />
QS 13–Promptitude; QS 14–Punctuality; QS 15–Safety.<br />
4. THE COST EQUATION<br />
The total cost of the purchased ICE car is:<br />
ICE<br />
P<br />
ICE ICE ICE<br />
( CP<br />
) + ( CP<br />
) ( CP<br />
) OM<br />
C = + [€/ICE car] (1)<br />
I<br />
The total cost of the purchased HSV is:<br />
( ) ( ) ( )<br />
HSV HSV HSV HSV<br />
P P<br />
I<br />
P<br />
C<br />
P<br />
OM<br />
C<br />
C = C + C + C [€/HSV car]<br />
The total cost of the ICE car transport service is:<br />
ICE<br />
S<br />
ICE ICE ICE<br />
( CS<br />
) ( S ) ( S ) I + C C C OM<br />
C = + [€/km ICE] (3)<br />
The total cost of the HSV transport service is:<br />
HSV<br />
S<br />
HSV HSV HSV<br />
( CS<br />
) + ( CS<br />
) ( CS<br />
) OM<br />
C = +<br />
[€/km HSV] (4)<br />
Taking into consideration that:<br />
ICE ICE ICE<br />
( C ) C / τ<br />
S<br />
I<br />
P<br />
I<br />
C<br />
(2)<br />
= [€/km ICE] (5)<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 50
HSV HSV HSV<br />
( C ) C / τ<br />
S<br />
I<br />
= [€/km HSV] (6) S = 0<br />
(18)<br />
P<br />
ICE<br />
ICE ICE<br />
( C ) sOM<br />
( CS<br />
) I<br />
S<br />
OM<br />
= [€/km ICE] (7) In this case<br />
HSV<br />
HSV HSV<br />
( C ) sOM<br />
( CS<br />
) I<br />
S<br />
OM<br />
= [€/km HSV]<br />
1+<br />
s<br />
(8)<br />
= 1+<br />
s<br />
ICE ICE ICE<br />
( CS ) f c f / 100<br />
C<br />
= [€/ km ICE] (9)<br />
HSV HSV ICE ICE<br />
( ) k f c / 100<br />
km<br />
HSV<br />
HSV HSV HSV HSV HSV<br />
( OM )( [ 1+<br />
pOM<br />
)( C p ) + ( C p ) ] / τ<br />
I<br />
c<br />
ICE ICE ICE ICE ICE<br />
( OM )( [ 1+<br />
pOM<br />
)( C p ) + ( C p ) ] / τ +<br />
I<br />
c<br />
HSV ICE ICE<br />
( k −1)<br />
f c / 100<br />
+ [€ / km] (19)<br />
C S C = f<br />
f [€/ km HSV] (10)<br />
ICE ( C P ) OM<br />
HSV ( C P ) OM<br />
ICE ICE<br />
= pOM<br />
( CP<br />
) I [€ / ICE car]<br />
HSV HSV<br />
= pOM<br />
( CP<br />
) I [€ / HSV]<br />
(11)<br />
(12)<br />
the expression of ICE transport cost becomes:<br />
ICE<br />
ICE<br />
ICE ICE ICE ICE<br />
C S = ( 1 + sOM<br />
) [( 1 + pOM<br />
) ( C P ) I + ( C P ) C]<br />
/ τ<br />
ICE ICE<br />
+ f c f / 100 [€/km ICE] (13)<br />
+<br />
while that of HSV transport cost is:<br />
HSV<br />
HSV<br />
HSV HSV<br />
C S = ( 1 + sOM<br />
) [( 1+<br />
pOM<br />
) ( CP<br />
) I +<br />
HSV HSV HSV ICE ICE<br />
+ ( C P ) C]<br />
/ τ + k f f c f / 100<br />
[€/km HSV] (14)<br />
5. THE STATE SUBSIDY<br />
Knowing that presently the ICE transport is cheaper<br />
than that of HSV:<br />
ICE<br />
C S<br />
HSV<br />
< CS<br />
[€ / km] (15)<br />
to encourage the development of HSV research <strong>and</strong><br />
development it is necessary the subsidy km<br />
S HSV , so<br />
that:<br />
ICE km<br />
C S + S HSV<br />
HSV<br />
= CS<br />
[€ / km] (16)<br />
From equations (13), (14) <strong>and</strong> (16) we can obtain the<br />
expression of the necessary subsidy:<br />
km<br />
HSV HSV HSV<br />
S HSV = ( 1+<br />
sOM<br />
)( [ 1+<br />
pOM<br />
)( C p ) +<br />
I<br />
HSV HSV<br />
ICE ICE ICE<br />
+ ( C p ) ] / τ − ( 1+<br />
sOM<br />
)( [ 1+<br />
pOM<br />
)( C p ) +<br />
c<br />
I<br />
ICE ICE HSV ICE ICE<br />
+ ( C p )] / τ + ( k f −1<br />
) f c f / 100<br />
c<br />
[€/km] (17)<br />
6. THE PROFITABLENESS OF HSV<br />
The relation (17) is essential when analyzing the<br />
profitableness of HSV. It allows us to see the<br />
influence of the main factors, to find out how could<br />
we give up to subsidy km<br />
7. MATHEMATICAL MODELING, RESULTS<br />
AND DISCUSSION<br />
In the reference papers [1, 20] we found reasons to<br />
HSV<br />
ICE HSV<br />
ICE<br />
consider C p = 1.<br />
3C<br />
p ; ( C p ) = ( C p ) ;<br />
OM<br />
OM<br />
ICE HSV HSV<br />
τ = 0 . 8τ<br />
; k f = 0.<br />
6...<br />
0.<br />
8 ;<br />
ICE<br />
c f = 1.77...3.54 €/l fuel.<br />
These data are argued below. From [20] we can read:<br />
“<strong>Hybrid</strong> vehicles do cost more than their gasoline-only<br />
counterparts. On average, the price premium is $2,500 to<br />
$3,000. Buyers, however, do have the benefit of a $2,000<br />
federal tax deduction for purchasing a hybrid as part of the<br />
Internal Revenue Service's Clean Fuel Vehicle deduction.<br />
The deduction, which was put into place as an incentive<br />
for consumers to consider this new technology, was<br />
scheduled to decline gradually beginning in 2004 <strong>and</strong><br />
eventually be phased out. Congress has extended this<br />
cre<strong>di</strong>t, however, offering up to a $2,000 tax cre<strong>di</strong>t on<br />
hybrids placed into service in 2004 <strong>and</strong> 2005. The cre<strong>di</strong>t<br />
drops to $500 for 2006.<br />
Boughey received the $2,000 federal deduction as well as<br />
a state deduction of $3,600, which was calculated based on<br />
his purchase of a hybrid as well as on the vehicle he<br />
replaced — a 1991 Mercury Gr<strong>and</strong> Marquis that was sold<br />
for salvage.<br />
For comparison purposes, Laumann calculated first-year<br />
insurance costs for all the versions of the 2004 Honda<br />
Civic four-door sedan inclu<strong>di</strong>ng the Civic <strong>Hybrid</strong>. Costs<br />
ranged from $835 to $849 for an average driver in the state<br />
of California with the Civic <strong>Hybrid</strong> falling near the middle<br />
at $844.<br />
Like the other automakers, Toyota has also done a lot of<br />
testing of its hybrid-specific components. Its battery packs<br />
in particular have lasted for over 180,000 miles in testing.<br />
"We've looked at all the things that put stress on batteries,<br />
such as the <strong>di</strong>scharge/charge cycles <strong>and</strong> extreme<br />
temperatures," says Dave Hermance, executive engineer<br />
for environmental technology at Toyota.<br />
When it comes to regular maintenance, most hybrids do<br />
not require any maintenance on the hybrid-specific<br />
components. One notable exception is an air filter on the<br />
Ford Escape <strong>Hybrid</strong>. "The air filter for the battery system<br />
needs to be replaced every 40,000 miles," explained<br />
Olson.<br />
The gasoline engine in a hybrid requires the same<br />
maintenance that it would if it were the only power source<br />
in the vehicle. That means oil changes every 5,000 to<br />
10,000 miles depen<strong>di</strong>ng on the vehicle <strong>and</strong> the driving<br />
con<strong>di</strong>tions.<br />
Another component that regularly needs to be replaced on<br />
S HSV , making the HSV<br />
profitable. For this, we have to consider<br />
every vehicle is the brake pads, but with hybrids these last<br />
much longer thanks to regenerative braking. In<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 51<br />
f<br />
f<br />
=
egenerative braking, the electric motor becomes a<br />
generator <strong>and</strong> captures the energy that would be lost<br />
as heat through the brakes when the vehicle's brakes<br />
are applied or when it is coasting. Once the energy is<br />
captured, it is transformed into usable electricity,<br />
which recharges the batteries <strong>and</strong> in turn increases<br />
the number of miles than can be traveled per gallon<br />
of gasoline. An added benefit is that the reduced heat<br />
means less wear <strong>and</strong> tear on the brakes, which means<br />
that they don't need to be replaced as often as<br />
conventional brakes. "We've seen customers go<br />
85,000 miles before they needed to replace their<br />
brakes on their Prius vehicles," says Toyota's<br />
Hermance.<br />
One of the top reasons that people purchase a hybrid<br />
vehicle is to get better fuel economy <strong>and</strong> they are<br />
often <strong>di</strong>sappointed that they don't experience the fuel<br />
economy numbers listed on the window sticker in<br />
their regular driving. "I just love my Honda Civic<br />
<strong>Hybrid</strong>, but I have been a bit <strong>di</strong>sappointed that the<br />
gas mileage isn't better," says Ivey Doyal of Atlanta,<br />
Ga.<br />
To be sure, <strong>di</strong>fferences in projected fuel economy<br />
versus real-world driving can mean serious<br />
<strong>di</strong>fferences in your wallet over the long term.<br />
Unfortunately, there is a <strong>di</strong>screpancy between the<br />
EPA's fuel economy ratings, which are listed on the<br />
window sticker when you buy a new car or truck,<br />
<strong>and</strong> the real-world results that most drivers<br />
experience, regardless of the type of vehicle they<br />
drive. The EPA's ratings are the numbers<br />
manufacturers are required by law to list in all the<br />
promotional materials for their vehicles.<br />
Unfortunately, the procedure the EPA uses to<br />
calculate these numbers is outdated <strong>and</strong> isn't<br />
in<strong>di</strong>cative of the way most Americans drive today.<br />
The EPA has made adjustments to its calculations to<br />
try to compensate for this. Even with these<br />
adjustments, however, the numbers still often <strong>di</strong>ffer<br />
from the real world. "We've seen where the typical<br />
driving style can be as much as 20-percent less than<br />
the EPA fuel economy number," says Bienenfield.<br />
While all vehicles are affected by this <strong>di</strong>screpancy,<br />
hybrid vehicles have the appearance of being<br />
affected even more so. "For example," explains<br />
Bienenfield, "A vehicle that has a fuel economy<br />
rating of 20 mpg may only get 18 mpg, while a<br />
vehicle that is rated at 50 mpg may only get 45 mpg.<br />
This seems like a bigger issue for the more fuelefficient<br />
vehicle, but in reality both vehicles are off<br />
by 10 percent."<br />
In the informal survey we <strong>di</strong>d with Honda <strong>and</strong><br />
Toyota hybrid owners, fuel economy numbers<br />
ranged from 33 to 49 mpg on average, which<br />
reflected many driving styles <strong>and</strong> a wide range of<br />
commutes. While these numbers are significantly<br />
lower than the EPA ratings, all the owners we<br />
interviewed were happy overall with the fuel<br />
economy as it is still better than most gasoline-only<br />
vehicles.<br />
Perhaps what is most mislea<strong>di</strong>ng about the fuel<br />
economy ratings is that they don't show how widely<br />
numbers can vary based on an in<strong>di</strong>vidual's typical<br />
driving route. "Short trips are the harshest on fuel<br />
economy, so anyone who drives just a few miles in<br />
his typical trip will see lower mpg numbers than<br />
someone who drives, say, 15 miles to work," says<br />
Bienenfield. Our unscientific poll showed these results as<br />
well. Pittsburgh, Pa., resident Jen Bannan typically drives<br />
just a few miles in each trip <strong>and</strong>, as a result, had the lowest<br />
fuel economy of those we interviewed, averaging 33 mpg<br />
in her 2002 Toyota Prius. "Is (the lower fuel economy)<br />
<strong>di</strong>sappointing? Sure, but I'm still filling up less than I <strong>di</strong>d<br />
in my old car <strong>and</strong> the Prius is the best car I've ever<br />
owned," she said.<br />
At the opposite end of the spectrum, Civic <strong>Hybrid</strong> driver<br />
Boughey <strong>and</strong> Honda Insight owner Dana Dorrity of Tivoli,<br />
N.Y., have commutes of 60 <strong>and</strong> 50 miles one way,<br />
respectively, on roads with rolling hills. Both had the<br />
highest fuel economy of those we spoke with, at 47 mpg<br />
for Boughey <strong>and</strong> 49 mpg for Dorrity. Poughkeepsie, N.Y.,<br />
resident Mary Koniz Arnold has no trouble averaging 50<br />
mpg in her 2001 Toyota Prius (which she bought used in<br />
April 2004) on longer trips, but she averages closer to 40<br />
mpg during her one-way commute of 10 miles.<br />
"To be fair," says Toyota's Hermance, "there is no way any<br />
two tests will give the range of consumer exposure in<br />
terms of driving con<strong>di</strong>tions <strong>and</strong> temperatures. He<br />
continued, "We are really measuring the wrong thing.<br />
Since you don't get to choose how many miles you drive,<br />
we should be measuring the gallons consumed."<br />
Rea<strong>di</strong>ng this large variety of documentary reasons, the<br />
reader can underst<strong>and</strong> better how <strong>di</strong>fficult was the authors’<br />
task to collect numerical data for their study.<br />
Finally the authors made the following hypotheses:<br />
HSV<br />
ICE<br />
ICE<br />
OM<br />
p OM OM ; ( p ) I ( p ) I<br />
HSV ICE<br />
s = 0.<br />
07 ; s OM = 0.<br />
05 ; C P = 10000 €;<br />
= = 0.<br />
40<br />
ICE<br />
HSV<br />
p<br />
HSV<br />
C<br />
ICE<br />
= 1.<br />
2 C ;<br />
τ<br />
= τ = 75000 km ICE or HSV / ICE car or<br />
HSV<br />
C<br />
ICE<br />
= 1.<br />
2 C ;<br />
ICE<br />
f = 7 l / 100 km<br />
HSV; ( p ) ( p ) C<br />
C<br />
ICE ICE<br />
ICE; ( C ) = 04 . C ; ( ) = 04<br />
p<br />
I<br />
p<br />
C . C<br />
ICE ICE<br />
p<br />
C<br />
p<br />
By using these data <strong>and</strong> the mathematical model<br />
km<br />
S<br />
HSV<br />
τ (fig. 1),<br />
previously presented, the functions HSV ( )<br />
km ICE<br />
SHSV ( c f ) (fig. 2),<br />
km<br />
HSV ( HSV<br />
f )<br />
S k (fig. 3), were<br />
calculated.<br />
From the fig. 1 we can see how the state unitary subsidy of<br />
100 km<br />
HSV S HSV [€ / 100 km] is influenced by total life cycle<br />
of a HSV, τ HSV [km HSV/ HSV car]. The <strong>di</strong>agram was<br />
calculated with the values previously in<strong>di</strong>cated <strong>and</strong><br />
inserted in <strong>di</strong>agram field. The compared cost-to-quality<br />
analysis applied here shows us that:<br />
100 km<br />
1) The state unitary subsidy S HSV [€ / 100 km] is<br />
lowering when the total life cycle of HSV τ HSV [km HSV/<br />
HSV car] is increasing. In other words, the more resistant<br />
in time is HSV, the less is the necessary unitary state<br />
subsidy. How much must be this total life cycle of HSV so<br />
that the state subsidy to not be necessary? The calculus<br />
results shows HSV<br />
ICE<br />
τ = 830000 km for τ = 75000 km <strong>and</strong><br />
HSV<br />
τ =101500 km when ICE<br />
τ = 93750 km. Of course,<br />
these results are unacceptable, we must have in view other<br />
practical solutions, like the fuel reduction ratio HSV<br />
k f<br />
increasing or to manufacture cheaper the HSV (the<br />
valueC<br />
).<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 52<br />
HSV<br />
P
2) The fig. 1 <strong>di</strong>agram shows also that the less is the<br />
ICE<br />
total life cycle of the ICE cars (the value τ ) the<br />
, [€/100km]<br />
100 km<br />
SHSV<br />
The state unitary subsidy of HSV,<br />
155<br />
150<br />
145<br />
140<br />
135<br />
130<br />
125<br />
120<br />
Fig. 1. The necessary subsidy<br />
, [€/100km]<br />
100 km<br />
SHSV<br />
The state unitary subsidy of HSV,<br />
unitary state subsidy<br />
The total life cycle of a HSV, HSV<br />
τ [km HSV / HSV car]<br />
100 km<br />
S HSV [€ / 100 km] is lower.<br />
7.5 8 8.5 9 9.5<br />
x 10 4<br />
115<br />
2<br />
1.5<br />
1<br />
0.5<br />
100 km<br />
S HSV [€/100km] versus the total life cycle of HSV HSV<br />
τ [km HSV / HSV car].<br />
100 km<br />
HSV<br />
HSV<br />
ICE<br />
C P =13000 €; C P = 10000 €;<br />
HSV<br />
ICE<br />
s OM = 0.<br />
07 ; s OM = 0.<br />
05 ; = = 0.<br />
40<br />
ICE<br />
HSV<br />
p OM pOM<br />
;<br />
HSV<br />
HSV<br />
( C p ) = 4800 €; ( C )<br />
I<br />
p = 4800 €;<br />
C<br />
ICE<br />
ICE<br />
( C p ) = 4000 €; ( C )<br />
I<br />
p = 4000 €;<br />
C<br />
ICE<br />
f = 7 l / 100 km; HSV<br />
k f<br />
ICE<br />
= 0.8; c f = 1 € / l<br />
ICE<br />
τ =75000 km<br />
100 km<br />
S HSV = 0 for HSV<br />
τ =83 10 4 km<br />
HSV<br />
ICE<br />
C P =13000 €; C P = 10000 €;<br />
HSV<br />
ICE<br />
s OM = 0.<br />
07 ; s OM = 0.<br />
05 ; = = 0.<br />
40<br />
ICE<br />
HSV<br />
p OM pOM<br />
;<br />
HSV<br />
HSV<br />
ICE<br />
( C p ) = 4800 €; ( C )<br />
I<br />
p = 4800 €; ( C p ) = 4000 €;<br />
C<br />
I<br />
ICE ( C p ) = 4000 €;<br />
C<br />
HSV<br />
k f =0.8<br />
ICE<br />
ICE<br />
f = 7 l / 100 km; τ = HSV<br />
τ =75000 km<br />
ICE<br />
c f =1 € / l<br />
100 km<br />
S HSV = 0 for HSV<br />
τ =101.5 10 4 km<br />
ICE<br />
τ =93750 km<br />
ICE<br />
c f =2 € / l<br />
0<br />
0.5 0.55 0.6 0.65 0.7 0.75 0.8<br />
Fig. 2. The necessary subsidy S [€/ 100 km] versus the fuel reduction ratio HSV<br />
The fuel reduction ratio of HSV,<br />
k of HSV.<br />
HSV<br />
k f<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 53<br />
f
Fig. 2 gives some answers to the questions arisen<br />
when examining the fig. 1.<br />
1). The first conclusion at glance is that with the gas<br />
ICE<br />
price c f = 1 € / l, if we can obtain HSV<br />
k f = 0.57,<br />
the HSV manufacturing <strong>and</strong> sale does not need state<br />
subsidy.<br />
2). The second conclusion is that the state subsidy<br />
100 km<br />
S increases when we use lesser the PV panels<br />
HSV<br />
(the value HSV<br />
k f is bigger).<br />
3). The third conclusion is that when the gas price<br />
ICE<br />
100 km<br />
c f increases, the state subsidy S is lowering,<br />
HSV<br />
becoming even zero if the fuel reduction ratio of<br />
HSV<br />
HSV, k f = 0,787 <strong>and</strong> this price reaches to<br />
ICE<br />
c f =2 € / l.<br />
, [€/100km]<br />
100 km<br />
SHSV<br />
The state unitary subsidy of HSV,<br />
2<br />
1.5<br />
1<br />
0.5<br />
HSV<br />
k f<br />
Fig. 3. The necessary subsidy<br />
8. FINAL CONCLUSION<br />
Accor<strong>di</strong>ng to the done study there is a real feasible<br />
solution to make HSV profitable in the next future.<br />
This solution is characterized by the following<br />
numerical parameters:<br />
HSV<br />
1. The total cost of HSV C P =13000 €;<br />
2. The total cost of classical car, powered by internal<br />
ICE<br />
combustion engine, C P = 10000 €;<br />
4. The operation-maintenance ratio of ICE car<br />
ICE<br />
service (eq. 7), s = 0.<br />
05 ;<br />
OM<br />
= 0.<br />
8<br />
HSV<br />
k f<br />
= 0.<br />
7<br />
Fig. 3 is showing intuitional conclusions:<br />
100 km<br />
1) The necessary subsidy S , [€/100 km] decreases<br />
HSV<br />
ICE<br />
when the unitary fuel cost c f [€ / l fuel] increases.<br />
100 km<br />
2) The necessary subsidy S , [€/100 km] must be<br />
HSV<br />
bigger when utilizing more solar energy ( HSV<br />
k f is<br />
decreasing).<br />
3) There are feasible situations when the necessary subsidy<br />
S , [€/100 km] can annul. The fig. 3 <strong>di</strong>agram shows<br />
100 km<br />
HSV<br />
HSV<br />
three such situations: k = 0.<br />
8 <strong>and</strong> ICE<br />
c = 1,1 [€ / l<br />
f<br />
HSV<br />
fuel] ; k = 0.<br />
7 <strong>and</strong> ICE<br />
c = 1,4 [€ / l fuel] <strong>and</strong><br />
HSV<br />
f<br />
k = 0.<br />
6 with<br />
HSV<br />
k f<br />
f<br />
ICE<br />
c f = 2,2 [€ / l fuel].<br />
HSV<br />
ICE<br />
C P =13000 €; C P = 10000 €;<br />
HSV<br />
ICE<br />
s OM = 0.<br />
07 ; s OM = 0.<br />
05 ; = = 0.<br />
40<br />
ICE<br />
HSV<br />
p OM pOM<br />
;<br />
HSV<br />
HSV<br />
( C p ) = 4800 €; ( C )<br />
I<br />
p = 4800 €;<br />
C<br />
ICE<br />
ICE<br />
( C p ) = 4000 €; ( C )<br />
I<br />
p = 4000 €;<br />
C<br />
ICE<br />
ICE<br />
f = 7 l / 100 km; τ = HSV<br />
τ =75000 km<br />
0<br />
1 1.5 2 2.5<br />
100 km<br />
S , [€/100 km] versus the unitary fuel cost<br />
HSV<br />
f<br />
ICE<br />
c f [€ / l fuel].<br />
5. The operation-maintenance ratio of ICE car product,<br />
p (eq. 11) <strong>and</strong> p -the operation-maintenance ratio<br />
ICE<br />
OM<br />
HSV<br />
OM<br />
HSV<br />
of HSV product, (eq. 12) p OM pOM<br />
= 0.<br />
40 ;<br />
HSV<br />
C = 4800 €;<br />
= ICE<br />
6. The investment cost of the HSV, ( p )<br />
3. The operation-maintenance ratio of HSV car service<br />
HSV<br />
(eq. 8), s OM = 0.<br />
07 ;<br />
HSV<br />
C = 4800<br />
7. The consumption cost of the HSV, ( p )<br />
€;<br />
= 0.<br />
6<br />
The unitary fuel cost,<br />
ICE<br />
c f [€ / l fuel]<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 54<br />
f<br />
I<br />
C
8. The investment cost of the ICE car,<br />
ICE<br />
C = 4000 €;<br />
( p )<br />
I<br />
9. The consumption cost of the ICE car,<br />
ICE<br />
C = 4000 €;<br />
( p )<br />
C<br />
ICE<br />
10. The unitary fuel consumption of ICE, f = 7 l<br />
/ 100 km;<br />
ICE<br />
11. The total life cycle of an ICE car, τ [km ICE<br />
/ ICE car] <strong>and</strong> HSV<br />
τ -the total life cycle of a HSV,<br />
ICE<br />
[km HSV / HSV car] τ = HSV<br />
τ =75000 km.<br />
Of course, this is only one of the possible solutions.<br />
The done mathematical model presented here allows<br />
the modeling accor<strong>di</strong>ng to concrete possibilities the<br />
manufacturer has in order to achieve a better <strong>and</strong><br />
better HSV. Modeling so, using the compared costto-quality<br />
analysis as work procedure, the authors are<br />
convinced that the best solution of a HSV is an ideal<br />
[12, 16, 17, 18], untouchable as any ideal, but an aim<br />
point for researchers.<br />
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2000, Napoli 2001, Procee<strong>di</strong>ngs on CD.<br />
12. Ionita C.I. (2003), Engineering <strong>and</strong> Economic<br />
Optimization of Energy Production, International<br />
Journal of Energy Research, Article Reference No.<br />
811, Journals Production Dept., 26: 697-715 (DOI:<br />
10.1002/er.811), John Wiley & Sons, Ltd, Chichester,<br />
UK.<br />
13. Ionita C.I., The Cost-to-Quality Ratio Based<br />
Optimization of the Energy Production, Entropie nr.<br />
232, 2001, pp. 10-19.<br />
14. Ioniţă, C.I., Exten<strong>di</strong>ng thermo-economic analysis by<br />
cost to quality optimisation. Procee<strong>di</strong>ngs of ECOS<br />
2002 July 3-5, 2002, Berlin, Germany, pp. 1434-1441.<br />
15. Ionita C.I, Ion V.I. Cost-to-Quality Optimization of<br />
Refrigeration, NATO Advanced Study Institute, June<br />
23-July 5, 2002, Altin Yunus-Cesme, Izmir-Turkiye,<br />
An International Meeting, Co-Directors: Prof S.<br />
Kakac <strong>and</strong> Prof. H. Smyrnov, ASI No.978410.<br />
16. Ionita C.I., From Energy Analysis to Compared Costto-Quality<br />
Analysis of the Thermal Systems, Technical<br />
Sciences Academy of Romania, (2003), MOCM-9vol.2,<br />
pp.149-155, ISSN 1224-7480.<br />
17. Ionita C.I., Thermal Systems Optimization <strong>and</strong> Cost-to-<br />
Quality Analysis, International Journal of Heat <strong>and</strong><br />
Technology”, vol. 22 nr. 1, 2004 pp. 27-37.<br />
18. Ionita C.I., Beyond thermo-economic analysis of<br />
thermal systems: the compared cost-to-quality<br />
analysis, 1 st International Conference on Thermal<br />
Engines <strong>and</strong> Environmental Engineering, METIME<br />
2005, June 3-4, 2005, Galati, Romania.<br />
19. http://www.toyota.co.jp/en<br />
20. The Real Costs of Owning a <strong>Hybrid</strong>.<br />
www.edmunds.com/advice/fueleconomy/articles/103708/a<br />
rticle.html- 44k<br />
21. http://www.hybrid-car.org/<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 55
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 56
TECHNICAL AND ECONOMICAL FEASIBILITY STUDY OF A SMALL HYBRID VEHICLE FOR<br />
URBAN TRANSPORTATION<br />
C. Boccaletti (*) , G. Fabbri (*) , F. M. Frattale Mascioli (§) , E. Santini (*)<br />
(*) Department of Electrical Engineering, University of Rome “La Sapienza”<br />
(§) Department INFOCOM, University of Rome “La Sapienza”<br />
Abstract: A technical <strong>and</strong> economical study has been carried out by the authors in order to<br />
assess the feasibility of the hybri<strong>di</strong>sation of a small vehicle for urban transportation. An<br />
existing commercial vehicle powered by a 4kW internal combustion engine has been<br />
taken as a reference. A possible layout of the new hybrid propulsion system has been<br />
stu<strong>di</strong>ed. Weights <strong>and</strong> volume occupancy have been examined. Initial <strong>and</strong> operating costs<br />
have been estimated <strong>and</strong> compared with the present market costs of the original vehicle.<br />
Performance calculations allowed to evaluate the vehicle behaviour in a st<strong>and</strong>ard mission<br />
<strong>and</strong> management aspects have been <strong>di</strong>scussed. Copyright © 2002 IFAC<br />
Keywords: <strong>Hybrid</strong> Electric <strong>Vehicles</strong>, Urban transportation.<br />
1. INTRODUCTION<br />
In the last years the public perception of aspects<br />
related to the quality of life in urban centres has<br />
increased considerably, con<strong>di</strong>tioning the in<strong>di</strong>vidual<br />
choices <strong>and</strong> the administration policies. As a<br />
consequence, technical issues arising from the need<br />
to reduce the polluting emissions of vehicles are<br />
more <strong>and</strong> more important. Accor<strong>di</strong>ng to the latest<br />
available national (Italian) data, road transportation<br />
is responsible for the higher percentage of NOx, CO<br />
<strong>and</strong> Non-Methanic-Volatile-Organic-Compounds<br />
(NMVOC) emissions, as shown in Table 1. If the<br />
contribution of these pollutants is splitted accor<strong>di</strong>ng<br />
to the type of vehicles, one can see that passenger<br />
cars are the main source of polluting emissions.<br />
For this reason, the problem of air quality trusted in<br />
the last years the dem<strong>and</strong> for vehicles with a low<br />
impact to the environment (C. Boccaletti, L.<br />
Martellucci, 2001, K. Rajashekara et al., 2002, K.<br />
Rajashekara, 2004). Moreover, urban areas with<br />
restricted access are wider <strong>and</strong> wider, aiming to<br />
reduce the air pollution. Since these areas are usually<br />
those with the most intense traffic <strong>and</strong> the lowest<br />
number of parking places, the vehicle size is also<br />
important (F. Caricchi et al., 2003). In the following,<br />
a technical <strong>and</strong> economical study to assess the<br />
feasibility of the hybri<strong>di</strong>sation of a small vehicle for<br />
urban transportation is described.<br />
2. THE ORIGINAL VEHICLE<br />
The vehicle chosen for the hybri<strong>di</strong>sation is a small<br />
commercial vehicle suitable for city service (see<br />
Fig. 1). This kind of vehicle is particularly conceived<br />
to be used in the narrow streets of historical centres<br />
<strong>and</strong> to make parking easier. The technical data <strong>and</strong><br />
size of the vehicle are listed in Tabs 2 <strong>and</strong> 4,<br />
respectively. Two points of the characteristic curve<br />
are reported in Tab. 3.<br />
The engine <strong>and</strong> the other components of the existing<br />
(tra<strong>di</strong>tional) propulsion system are located in the<br />
front. Owing to the reduced size of the vehicle, the<br />
various element are <strong>di</strong>sposed in such a way that the<br />
volume occupancy is optimised <strong>and</strong> the insertion of a<br />
new bulk elements would be <strong>di</strong>fficult. The bonnet or<br />
the load deck (in the pickup version) are located in<br />
the rear. A mean market price of the vehicle range of<br />
8000 € can be taken as a reference.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 57
Table 1 Contribution of road transportation to<br />
polluting emissions in 2002 (%)<br />
SOX NOX NMVOC CO CO2<br />
1.95 48.81 32.31 65.27 27.85<br />
Source: Elaboration from Apat (2006)<br />
Fig. 1. The commercial vehicle chosen for the<br />
hybri<strong>di</strong>sation.<br />
Table 2 Features of the commercial vehicle chosen<br />
for the hybri<strong>di</strong>sation<br />
Engine Diesel<br />
N° cylinders 2<br />
Cyl. Volume 505 cm 3<br />
Cycle 4 Strokes<br />
Cooling Type liquid<br />
Max. Power 4 kW @ 3600 rpm<br />
Max. Torque 14 Nm @ 2400 rpm<br />
Transmission continuous variator with pulleys<br />
<strong>and</strong> centrifugal masses<br />
Gear Position Inward / Backward / Idle<br />
Traction Front wheels with inverter<br />
<strong>di</strong>fferential<br />
Electric Circuit 12 V<br />
Voltage<br />
Max. Speed 45 Km/h<br />
Max. Slope > 25%<br />
Table 3 Performance data of the commercial vehicle<br />
chosen for the hybri<strong>di</strong>sation<br />
rpm Torque [Nm] Power [W]<br />
2400 14 3500<br />
3600 10.61 4000<br />
Table 4 Size <strong>and</strong> weight of the commercial vehicle<br />
chosen for the hybri<strong>di</strong>sation<br />
Length 3224 mm Width 1378 mm<br />
Heigth 1487 mm Mass 349 kg<br />
Admissible Mass 675 kg<br />
3. THE HYBRIDISATION<br />
The expected benefits of the hybri<strong>di</strong>sation are:<br />
− Reduction of fuel consumption;<br />
− Reduction of polluting emissions;<br />
− Increased performance.<br />
3.1 Parallel configuration<br />
The first configuration of the hybrid system taken as<br />
a reference is of the parallel type. In general, this<br />
configuration is considered suitable for small<br />
vehicles. The scheme of the propulsion system<br />
includes a power-split drive train. Accor<strong>di</strong>ng to the<br />
complexity of such a device, together with other<br />
considerations, the choice of a parallel configuration<br />
could be not suitable to the series production in a<br />
small enterprise with affordable costs <strong>and</strong> therefore<br />
an acceptable commercial price. The configuration<br />
has been stu<strong>di</strong>ed for a specific use of the vehicle in<br />
an urban environment, with limited flexibility. In<br />
case of missions that are quite far from the city<br />
st<strong>and</strong>ards, the availability could not be assured. In the<br />
particular case of these vehicles, the Italian law<br />
prescribes a maximum speed of 45 km/h, therefore<br />
even the European st<strong>and</strong>ard for motorcycles (ECE47)<br />
could not be taken as a reference, because it provides<br />
a maximum speed of 50 km/h. However, nonconventional<br />
cycles have been proposed for the<br />
analysis of the vehicle behaviour in an urban<br />
environment, <strong>and</strong> among these one with a maximum<br />
speed of 45 km/h (Avella, 2000) (see Fig. 2). This<br />
cycle has been considered for our analyses.<br />
Fig. 2. Urban cycle in heavy traffic con<strong>di</strong>tions.<br />
Chosing a <strong>Hybrid</strong>isation Factor (HF) of 25%, the<br />
1 pole, 60 Hz syncronous motor has a power of<br />
1 kW. The electromagnetic torque is 3.18 Nm. The<br />
storage system should have a capacity of at least<br />
1.1 kWh. Lead-acid batteries (not too expensive, with<br />
a quite long life), inclu<strong>di</strong>ng supports <strong>and</strong><br />
connections, should weight about 30 kg, with a<br />
volume of 10 litres. The voltage is 48 V. Lithium<br />
batteries could be an alternative, with less weight <strong>and</strong><br />
volume occupancy. An inverter suitable for the<br />
application has the features of Table 5. Considering<br />
an efficiency of the charge/<strong>di</strong>scharge cycle of 80%<br />
<strong>and</strong> a battery charge efficiency of 90%, the electric<br />
energy consumption is about 1.5 kWh per cycle (i. e.,<br />
per day), correspon<strong>di</strong>ng to 0.26 € or 0.0052 €/km, if<br />
50 km is the mean daily run. The battery cost is some<br />
0.05 €/Wh. Therefore, 55 € are enough to ensure the<br />
provided run in the first period of operation.<br />
However, the capacity decreases of 0.04% per cycle,<br />
so that after 365 cycles (one year), the daily run is<br />
reduced. Usually, in this case the driver increases the<br />
frequency of the charge cycles instead of changing<br />
the batteries. In so doing, the battery aging becomes<br />
faster <strong>and</strong> faster. In the most favourable case, with an<br />
optimum management of charge/<strong>di</strong>scharge cycles,<br />
one can think to reach a battery life of 5 years, which<br />
corresponds to 2000 kWh stored <strong>and</strong> 475 €.<br />
Inclu<strong>di</strong>ng the initial cost of the batteries, the cost per<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 58
year is 106 € <strong>and</strong> 0.0058 €/km. The above costs are<br />
calculated with the electric propulsion as the only<br />
one. Considering a 20% saving in hybrid mode,<br />
thanks to the energy recovered during braking <strong>and</strong><br />
deceleration, the cost can be reduced to some<br />
0.0046 €/km. For city service, the cost of fuel is<br />
about 0.056 l/km, or 0.07 €/km. In hybrid mode, the<br />
fuel consumption can be reduced of about 20%,<br />
obtaining a cost of 0.05 €/km. Therefore, the total<br />
operation cost of the hybrid vehicle can be calculated<br />
in about 0.0546 €/Km. To redeem 2000 € (<strong>di</strong>fference<br />
with the price of a tra<strong>di</strong>tional vehicle), the vehicle<br />
should run for 130000 km, correspon<strong>di</strong>ng to about 7<br />
years at 50 km/day. During this period, one<br />
substitution of the batteries has to be considered<br />
(55 €). In conclusion, the complete amortization is<br />
attained at the end of the vehicle life. Therefore, the<br />
user should not benefit by significant economic<br />
advantages. On the other h<strong>and</strong>, from the point of<br />
view of the environmental impact a significant<br />
reduction of polluting emissions can be obtained.<br />
The above rough considerations, however, do not<br />
take the possible ad<strong>di</strong>tional production costs into<br />
account, due to the choice of the power-split drive<br />
train, needed for the coupling among the propulsion<br />
<strong>and</strong> traction devices (L. Martellucci et al., 2001).<br />
Moreover, the realisation of the drive <strong>and</strong> of the<br />
relevant control system could require particular<br />
technical skills, that are not always available in a<br />
small enterprise. Although quite simplified, the<br />
above results show that in this particular case the<br />
parallel configuration does not have wide margins of<br />
application, from both an industrial <strong>and</strong> customer’s<br />
point of view.<br />
Table 5 Inverter features<br />
Voltage 12 Vdc or 24 Vdc ±15%<br />
Power range<br />
300 VA÷12 kVA with<br />
intermittent service<br />
Efficiency 71-77%<br />
Output voltage 220 Vac<br />
In order to increase the availability of the hybrid<br />
vehicle also for missions quite far from the urban<br />
cycle taken as a reference in the design phase, a<br />
series configuration can be chosen. The latter<br />
includes more components to be located into the<br />
vehicle than the parallel solution, but the layout is<br />
subject to less constraints. Moreover, the components<br />
do not <strong>di</strong>ffer from commercial devices, whose<br />
assemblage requires st<strong>and</strong>ard technical skills.<br />
3.2 Series configuration<br />
As said above, in this configuration there is no<br />
mechanical coupling between the Internal<br />
Combustion Engine (ICE) <strong>and</strong> the wheels, reducing<br />
the constraints of the layout, <strong>and</strong> this is particularly<br />
important for a small vehicle. However, there are<br />
more components than in the parallel case, <strong>and</strong> more<br />
space is needed for the batteries. The volume of the<br />
engine bonnet in the original vehicle is not so large,<br />
so that the electric motor, the inverter <strong>and</strong> the<br />
batteries cannot be mounted in the same place. In<br />
Fig. 3 a sketch of the proposed layout is shown. The<br />
generator is positioned in the front engine bonnet, the<br />
electric motor is connected to the rear wheel axis,<br />
batteries <strong>and</strong> converters are in the rear coffin.<br />
Fig. 3. Layout of the hybrid series propulsion system.<br />
In order to choose the component size, the required<br />
performance have to be considered. As above stated,<br />
the vehicle has a maximum speed of 45 km/h. The<br />
aerodynamic, mechanical <strong>and</strong> rolling resistances - the<br />
latter inclu<strong>di</strong>ng both the rolling friction <strong>and</strong> the tyre<br />
deformation - contribute to the total resistance to the<br />
vehicle motion. Such a resistance can be calculated<br />
through expressions containing empirical<br />
coefficients. For our case, the rolling resistance is<br />
assumed proportional to vehicle weight W. The<br />
reference weight for the performance calculation is<br />
assumed to be 500 kg. It follows<br />
Ftyre = 8·g·W·10 -3 = 8·9.81⋅0.5 = 39.24 N (1)<br />
The aerodynamic resistance can be calculated as<br />
Faer=0.5·Cr·ρ·A·V 2 =0.5·0.3·1.2·2.0·12.5 2 =56.25 N (2)<br />
being Cr a drag coefficient, ρ the air density (kg/m 3 ),<br />
A the reference front section area (m 2 ) <strong>and</strong> V the<br />
maximum vehicle speed (m/s). Total resistance R is<br />
95.49 N. The correspon<strong>di</strong>ng torque at the wheels is<br />
T = R·d = 95.49·0.252 = 24.06 Nm (3)<br />
being d the wheel ra<strong>di</strong>us (m). Therefore, the required<br />
power is<br />
P = T·ω = 24.06·7.88·2π = 1.19 kW (4)<br />
Since the chosen electric motor has a rated power of<br />
4.2 kW, one can calculate the maximum slope the<br />
vehicle can climb at the maximum speed. The<br />
available ad<strong>di</strong>tional power is 3.01 kW. It follows<br />
Max Slope% = 3.01·3600/(500·9.81·45) = 4.9 (5)<br />
One can also calculate at what speed the vehicle can<br />
move up a slope of 10%, the st<strong>and</strong>ard value for<br />
continuous running. In this case the rated power of<br />
the electric motor allows to attain a speed of about<br />
26 km/h. Up a slope of 20% the maximum speed is<br />
about 15 km/h. The electric motor has a rated torque<br />
of 40 Nm @ 1000 rpm <strong>and</strong> a maximum torque of<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 59
80 Nm. The weight is about 15 kg <strong>and</strong> the height <strong>and</strong><br />
axial length are about 25 cm <strong>and</strong> less than 30 cm,<br />
respectively.<br />
The battery pack consists of 5 lead gel batteries of<br />
30 Ah, 12 V nominal. The total weight is 53 kg.<br />
Height, length <strong>and</strong> width of a single battery are<br />
15.5 cm, 19.5 cm <strong>and</strong> 13.3 cm, respectively. The noload<br />
voltage <strong>and</strong> the charge <strong>and</strong> <strong>di</strong>scharge resistances<br />
as a function of the State-Of-Charge (SOC) are given<br />
in Table 6.<br />
Table 6 Battery characteristics<br />
SOC V0 R<strong>di</strong>s Rchg<br />
0.1 11.28 0.0268 0.0373<br />
0.2 11.58 0.0163 0.0259<br />
0.3 11.88 0.0124 0.0201<br />
0.4 12.06 0.0107 0.0173<br />
0.5 12.18 0.01 0.0166<br />
0.6 12.36 0.01 0.017<br />
0.7 12.54 0.01 0.0196<br />
0.8 12.72 0.0114 0.0243<br />
0.9 13.02 0.0114 0.0348<br />
1 13.5 0.011 0.1141<br />
The <strong>di</strong>esel generator has the characteristics of<br />
Table 7. The efficiency can be evaluated through the<br />
curves of power <strong>and</strong> specific fuel consumption given<br />
by the manufacturer with reference to the ISO<br />
3046/1-IFN st<strong>and</strong>ard (see Fig. 4). The maximum<br />
efficiency corresponds to a power of about 4.1 kW.<br />
Table 7 Engine characteristics<br />
N° cylinders 1<br />
Cyl. Volume 315 cm 3<br />
Max. Power 5 kW @ 3600 rpm<br />
Max. Torque 15 Nm @ 2400 rpm<br />
Efficiency<br />
0.32<br />
0.31<br />
0.3<br />
0.29<br />
0.28<br />
0.27<br />
1.5 2 2.5 3<br />
Power [kW]<br />
3.5 4 4.5<br />
Fig. 4. Engine efficiency vs. engine power.<br />
In order to minimise the fuel consumption, a control<br />
strategy has to be chosen. Once fixed a SOC<br />
admissible range, the best operating point of the<br />
generator as a function of the power required by the<br />
drive is calculated minimising the fuel consumption<br />
(S. Barsali et al., 2002).<br />
When operating in ON-OFF mode, the DC source<br />
logic is based on the battery SOC. The optimisation<br />
procedure consists of calculating average drive<br />
power dem<strong>and</strong> Pd in a given time interval t,<br />
estimating the battery SOC in t, defining the<br />
operating state (ON or OFF) <strong>and</strong> finally calculating -<br />
if the state is ON - reference power Ps* as the power<br />
to be generated by the DC source correspon<strong>di</strong>ng to<br />
the maximum generation efficiency.<br />
Based on the values of Table 6, a global battery<br />
efficiency ηb = ηchgη<strong>di</strong>s has been estimated. A value<br />
of 0.85 has been assumed. The generation efficiency<br />
is defined as (S. Barsali et al., 2002)<br />
η<br />
( P + P − P ( 1−η<br />
) )<br />
d b b b<br />
gl = η<br />
(6)<br />
gen<br />
Ps<br />
The goal is to obtain the value of the average power<br />
to be delivered by the DC source as a function of<br />
average drive power dem<strong>and</strong> Pd. For each Pd, the<br />
value of Ps correspon<strong>di</strong>ng to the maximum of ηgl can<br />
be in<strong>di</strong>viduated. Thus, function Ps* = Ps*(Pd) can be<br />
obtained.<br />
An efficiency of 0.9 has been assumed for the DC<br />
(electric generator-inverter) generation system. The<br />
values of Fig. 4 have been multiplied by this<br />
efficiency, the procedure has been applied, <strong>and</strong> the<br />
curve of Fig. 5 has been obtained. It shows the<br />
average power to be delivered by the DC source vs.<br />
Pd, in order to have the minimum fuel consumption<br />
<strong>and</strong> to keep the battery SOC within the admissible -<br />
“safety” - range. Beyond the minimum point, on the<br />
right of the graph, the continuous operation (“load<br />
following”) substitutes the ON-OFF mode <strong>and</strong> no<br />
energy is stored in the battery pack.<br />
Source power [kW]<br />
4.5<br />
4.45<br />
4.4<br />
4.35<br />
4.3<br />
4.25<br />
4.2<br />
4.15<br />
4.1<br />
4.05<br />
4<br />
1.5 2 2.5 3<br />
Drive power [kW]<br />
3.5 4 4.5<br />
Fig. 5. Optimal DC source operation curve.<br />
The effects of the above control strategy on the<br />
global efficiency (M. Pasquali, G. Pede, 2006) are<br />
shown in Fig. 6.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 60
Global efficiency<br />
0.29<br />
0.285<br />
0.28<br />
0.275<br />
0.27<br />
0.265<br />
0.26<br />
0.255<br />
0.25<br />
0.245<br />
optimised<br />
load following<br />
0.24<br />
1.5 2 2.5 3<br />
Drive power [kW]<br />
3.5 4 4.5<br />
Fig. 6. Global generation efficiency curves in case of<br />
optimised control (blue) <strong>and</strong> load following<br />
(green).<br />
As above said, in the ON-OFF mode the generator<br />
operates in optimum con<strong>di</strong>tions <strong>and</strong> a part of the<br />
produced energy is stored in the batteries. From<br />
Fig. 5, one can see that the delivered power varies<br />
only between about 4.05 <strong>and</strong> 4 kW in the ON-OFF<br />
mode <strong>and</strong> within about 4 <strong>and</strong> 4.5 kW in the load<br />
following mode. However, for urban use the power<br />
dem<strong>and</strong> of this kind of vehicle can hardly overcome<br />
4 kW, unless ad<strong>di</strong>tional power is required by<br />
auxiliary devices (C. Boccaletti, L. Martellucci,<br />
2001). Thus, the generator practically operates at a<br />
fixed point, correspon<strong>di</strong>ng to the best efficiency. For<br />
a given vehicle mission, like that of Fig. 2, power Pb<br />
stored in the batteries can be calculated at every time<br />
instant, as the <strong>di</strong>fference between generated power Ps<br />
<strong>and</strong> drive power dem<strong>and</strong> Pd (see Fig. 6). An<br />
efficiency of 0.85 can be assumed for the electric<br />
drive. Accor<strong>di</strong>ng to the battery SOC, the generator<br />
should be switched on or off to keep the SOC within<br />
the fixed range, say 0.4÷0.85. In this way it is<br />
possible to calculate the energy produced by the<br />
generator during a complete charging/<strong>di</strong>scharging<br />
cycle of the batteries, <strong>and</strong> the relevant noise <strong>and</strong> fuel<br />
consumption. An evaluation of polluting emissions<br />
could be performed by means of maps given by the<br />
manufacturers, but an actual comparison with the<br />
dynamical behaviour of the original propulsion<br />
system is possible only on the basis of an<br />
experimental on-road campaign (Avella, 2000).<br />
However, a significant reduction of polluting<br />
emission is expected, thanks to the limited operating<br />
time of the engine, nearly in con<strong>di</strong>tions of best<br />
efficiency, covered <strong>di</strong>stances being equal.<br />
A software program has been set up to calculate the<br />
number of st<strong>and</strong>ard urban missions (<strong>and</strong> then the<br />
total covered <strong>di</strong>stance) correspon<strong>di</strong>ng to a complete<br />
charging/<strong>di</strong>scharging cycle of the batteries within the<br />
admissible range, <strong>and</strong> the relevant fuel consumption.<br />
A maximum noise level of 78 db has been calculated<br />
from manufacturer’s data, correspon<strong>di</strong>ng to the<br />
engine operating con<strong>di</strong>tions.<br />
Fig. 6. Main components <strong>and</strong> relevant power fluxes.<br />
Starting from the established minimum SOC level<br />
(0.4), the batteries are charged until the admissible<br />
limit. At that point the generator is switched off, <strong>and</strong><br />
the drive power dem<strong>and</strong> makes the stored energy<br />
decrease, until the minimum SOC is attained again.<br />
The charging/<strong>di</strong>scharging cycle of the batteries is<br />
completed in about 25 st<strong>and</strong>ard urban missions,<br />
correspon<strong>di</strong>ng to a <strong>di</strong>stance of 24.5 km. The fuel<br />
consumption is about 160 g, <strong>and</strong> the total produced<br />
energy is about 0.4 kWh. From the above<br />
considerations, it comes out that such configuration<br />
corresponds to a large flexibility <strong>and</strong> availability of<br />
the hybrid vehicle, allowing its use also for missions<br />
quite far from the urban cycle taken as a reference in<br />
the calculations.<br />
Finally, the cost of the main components of the new<br />
propulsion system can be estimated between 1750<br />
<strong>and</strong> 2250 €, accor<strong>di</strong>ng to the cost of the generator,<br />
being the cost of the battery pack some 250 € <strong>and</strong><br />
that of the electric drive some 500 €.<br />
4. CONCLUSIONS<br />
An existing commercial vehicle powered by a 4kW<br />
internal combustion engine has been taken as a<br />
reference for a preliminary technical – economical<br />
analysis of possible hybrid configurations. Weights,<br />
volume occupancy <strong>and</strong> costs of a parallel <strong>and</strong> a series<br />
layout have been estimated. A particular urban<br />
mission, suitable for this kind of vehicles in both<br />
configurations, has been in<strong>di</strong>viduated. Some aspects<br />
of the vehicle management have been <strong>di</strong>scussed with<br />
particular reference to the series configuration, <strong>and</strong><br />
performance calculations allowed to evaluate the<br />
characteristics of the propulsion system related to its<br />
availability also for missions quite far from the<br />
st<strong>and</strong>ard one. A significant reduction of polluting<br />
emission is expected in both cases with respect to the<br />
original (tra<strong>di</strong>tional) propulsion system. From both<br />
an industrial <strong>and</strong> customer’s point of view, in the<br />
particular case examined the series configuration<br />
seems to have wider margins of application, although<br />
a final answer could come only from more in-depth<br />
economical analyses.<br />
5. REFERENCES<br />
F. Avella (2000)– “L’attivita’ sperimentale della<br />
stazione sperimentale per i combustibili per la<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 61
valutazione delle emissioni generate dagli<br />
autoveicoli” – Proc. of Seminario ANPA, Rome,<br />
Italy (in Italian)<br />
L. Martellucci, M. Santoro, C. Boccaletti (2001) - “A<br />
Powertrain with Planetary Gear System:<br />
Advantages <strong>and</strong> a Design Approach” – Proc. of<br />
EVS 18 – The 18th International Electric<br />
Vehicle Symposium, Berlin, Germany<br />
C. Boccaletti, L. Martellucci (2001) – “Study of an<br />
air con<strong>di</strong>tioning system for a small hybrid<br />
vehicle based on the absorption principle” –<br />
SAE Paper 2001-01-3808, Proc. of Congresso<br />
SAE Brasil 2001, São Paulo, Brazil<br />
K. Rajashekara et al. (2002) - “Comparative study of<br />
new on-board power generation technologies for<br />
automotive applications,” in Proc. IEEE<br />
Workshop Power Electronics in Transportation,<br />
Auburn Hills, MI, pp. 3–10<br />
S. Barsali, M. Pasquali, G. Pede (2002) - "Definition<br />
of Energy Management Technique for Series<br />
<strong>Hybrid</strong> <strong>Vehicles</strong>" - Proc. of EVS 19 – The 19th<br />
International Electric Vehicle Symposium,<br />
Pusan, Korea<br />
F. Caricchi, L. Del Ferraro, F. Giulii Capponi, O.<br />
Honorati, E. Santini (2003) – “Three-Wheeled<br />
Electric Maxi-Scooter for Improved Driving<br />
Performances in Large Urban Areas” - Proc. of<br />
2003 IEEE International Electric Machines <strong>and</strong><br />
Drives Conference, IEMDC’03, Ma<strong>di</strong>son,<br />
Wisconsin, USA<br />
K. Rajashekara (2004) – “<strong>Hybrid</strong> <strong>and</strong> Fuel Cell<br />
Systems for Transportation”, Meeting IV of<br />
IEEE IASChapter, Hungary<br />
M. Pasquali, G. Pede (2006) – “Ottimizzazione della<br />
gestione energetica <strong>di</strong> un veicolo ibrido <strong>di</strong> tipo<br />
serie” – Proc. of 17th Seminario Interattivo<br />
ANAE, “Azionamenti Elettrici Evoluzione<br />
Tecnologica e Problematiche Emergenti”,<br />
Bressanone, Italy (in Italian)<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 62
PASSIVITY-BASED CONTROL OF HYBRID<br />
SOURCES APPLIED TO A TRACTION<br />
SYSTEM<br />
Damien Paire ∗ , Mohamed Becherif ∗∗ ,<br />
Abdellatif Miraoui ∗<br />
∗ L2ES, UTBM, Belfort (cedex) 90010, FRANCE<br />
∗∗ SeT, UTBM, Belfort (cedex) 90010, FRANCE<br />
damien.paire@utbm.fr<br />
Tel:+33(0)384583396, Fax:+33(0)384583413<br />
Abstract: Nowadays, energy management becomes an economic <strong>and</strong> technical<br />
issue. To reduce systems consumption, the idea is to recover energy when it<br />
is possible <strong>and</strong> to reuse it depen<strong>di</strong>ng on the dem<strong>and</strong>. To save energy, storage<br />
components (supercapacitors here) are needed to absorb or supply power picks.<br />
This article present an hybrid system suppling an electromotive force. In order<br />
to supervise the power flows in the system, Passivity-Based Control is used <strong>and</strong><br />
<strong>di</strong>fferent configurations are simulated.<br />
Keywords: energy recovery, hybrid system, Passivity-Based Control, embedded<br />
energy, supercapacitors<br />
1. INTRODUCTION<br />
In electric traction systems (like vehicles, elevators,...),<br />
iftheloa<strong>di</strong>ssupplied using a single energy<br />
source, it has to answer to all solicitations of<br />
the load. Thus, the source has to supply or absorb<br />
the picks of power resulting from accelerations <strong>and</strong><br />
braking. So, the source has to provide energy <strong>and</strong><br />
power, this is strongly penalizing. In order to optimize<br />
the power transfer <strong>and</strong> to improve equipment<br />
lifetime, supercapacitors (SC) <strong>and</strong> <strong>di</strong>fferent kind<br />
of DC sources can be hybri<strong>di</strong>zed. Then the SC<br />
supply or absorb power picks <strong>and</strong> the DC source<br />
provide the average power.<br />
In this paper, a hybrid power source using DC<br />
source (obtained from network or from batteries<br />
alone or associated with photovoltaic panels) <strong>and</strong><br />
SC supplying a load is proposed. In a first step,<br />
a dynamic modeling of the system is given. In<br />
a second step, this system is written in a Port<br />
Controlled Hamiltonian (PCH) form where im-<br />
portant structural properties are exhibited. Then<br />
a Passivity-Based Control (PBC) of the system is<br />
presented proving the global stability of the equilibrium<br />
with the proposed control laws. Finally,<br />
simulation results using Matlab are given.<br />
2. HYBRID DC SOURCE SYSTEM<br />
2.1 Structure of the hybrid source<br />
As shown in Figure 1, the stu<strong>di</strong>ed system comprises<br />
a DC link supplied by a DC source <strong>and</strong> a<br />
no reversible DC-DC Boost converter which maintains<br />
the DC voltage VDC to its reference value<br />
V DC <strong>and</strong> a SC storage device which is connected<br />
to the DC link through a current reversible DC-<br />
DC converter. The load consist of a resitor RL,<br />
a inductor LL <strong>and</strong> an electromotive force (emf)<br />
E. This structure is used to model merely an<br />
electrical machine.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 63
VN<br />
iN<br />
VSC<br />
LN iDC<br />
LDC iL<br />
TN<br />
iSC<br />
CSC<br />
CS<br />
LSC<br />
TSC<br />
VS<br />
T SC<br />
Fig. 1. System electrical model<br />
CDC<br />
VDC<br />
E<br />
LL<br />
RL<br />
The function of the DC source is to supply the<br />
mean power to the load, whereas the storage<br />
device is used as a power source: it supplies <strong>and</strong><br />
absorbs peak loads required during acceleration<br />
<strong>and</strong> braking. In order to manage energy exchanges<br />
between the DC link <strong>and</strong> the storage device, three<br />
operating modes are defined:<br />
• Charge mode, in which the main source supplies<br />
energy to the storage device,<br />
• Discharge mode, in which the storage device<br />
<strong>and</strong> the main source supply energy to the<br />
load,<br />
• Recovery mode, in which the load supplies<br />
energy to the storage device.<br />
2.2 State space model of the system<br />
The model of the hybrid system can be written<br />
in a state space model by choosing the following<br />
variables:<br />
x = � x1, x2, x3, x4, x5, x6, x7<br />
� T<br />
= � VS, iN ,VDC, iDC, VSC, iSC, iL<br />
The control vector is:<br />
u = � �T � �T u1, u2 = uN, uSC<br />
� T<br />
(1)<br />
where uN <strong>and</strong> uSC ∈ [0, 1].<br />
u = 1 means the associated transitor is closed <strong>and</strong><br />
u = 0 means the associated transitor is opened.<br />
The 7 th order overall state space model is then :<br />
˙x1 = 1<br />
CS<br />
˙x2 = 1<br />
LN<br />
[(1 − u1)x2 − x4]<br />
[VN − (1 − u1)x1]<br />
˙x3 = 1<br />
[x4 − x7 +(1−u2)x6] CDC<br />
˙x4 = 1<br />
[x1 − x3] (2)<br />
LDC<br />
˙x5 = −1<br />
x6<br />
CSC<br />
2.3 Equilibrium<br />
˙x6 = 1<br />
[x5 − (1 − u2)x3]<br />
LSC<br />
˙x7 = 1<br />
[x3 − RLx7 − E]<br />
LL<br />
y = x3<br />
After some simples calculations the equilibrium<br />
vector is:<br />
¯x = � ¯x1, ¯x2, ¯x3, ¯x4, ¯x5, ¯x6, ¯x7<br />
�<br />
= Vd, (Vd − E)Vd<br />
,Vd,<br />
RLVN<br />
Vd − E<br />
, ¯x5, 0,<br />
RL<br />
Vd − E<br />
RL<br />
Where Vd is the desired DC Bus voltage. An implicit<br />
purpose of the proposed structure (Figure 1)<br />
is to recover energy to charge the SC. Hence, the<br />
desired voltage ¯x5 = VSC(t =0)=12V .<br />
ū = � �<br />
�T ūN , ūSC =<br />
� T<br />
1 − VN<br />
, 1 −<br />
Vd<br />
¯x5<br />
�T Vd<br />
The natural energy function of the system is:<br />
(4)<br />
H = 1<br />
2 xT Qx (5)<br />
where Q = <strong>di</strong>ag{Cs; LN ; CDC; LDC; CSC; LSC; LL} is a<br />
<strong>di</strong>agonal matrix.<br />
3. PROBLEM FORMULATION<br />
After system modeling, equilibrium points are<br />
computed in order to ensure the desired behaviour<br />
of the system. When steady state is reached, the<br />
load has to be supplied only by the DC source. So<br />
the controller has to maintain the DC bus voltage<br />
to a constant value <strong>and</strong> the SC current has to be<br />
cancelled.<br />
During transient, the power delivered by the DC<br />
source has to be the more constant as possible<br />
(without a significant power peak), so the SC<br />
deliver the transient power to the load. If the<br />
load provide current, the SC recover its energy.<br />
At equilibrium, the SC has to be charged <strong>and</strong> the<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 64<br />
(3)<br />
� T
current has to be equal to zero.<br />
In the next section, a controller will be found <strong>and</strong><br />
the system’s stability will be prouved.<br />
4. PORT-CONTROLLED HAMILTONIAN<br />
REPRESENTATION OF THE SYSTEM<br />
PCH systems were introduced by [1] <strong>and</strong> has<br />
since grown to become a large field of interest in<br />
the research of electrical, mechanical <strong>and</strong> electromechanical<br />
systems. A recent <strong>and</strong> very interesting<br />
approach to solve these problems is the IDA-PBC<br />
method, which is a general way of stabilizing a<br />
large class of physical systems, see [2, 4].<br />
The desired closed loop energy function is:<br />
Hd = 1<br />
2 ˜xT Q˜x (6)<br />
where ˜x = x − ¯x is the new state space defining<br />
the error between the state x <strong>and</strong> its equilibrium<br />
value ¯x. So accor<strong>di</strong>ng to the state space model (2),<br />
the following equations can be written:<br />
˙˜x1 = 1<br />
CS<br />
˙˜x2 = 1<br />
LN<br />
˙˜x3 = 1<br />
CDC<br />
˙˜x4 = 1<br />
LDC<br />
[(1 − u1)(˜x2 +¯x2) − ˜x4 − ¯x4]<br />
[VN − (1 − u1)(˜x1 +¯x1)]<br />
˙˜x5 = −1<br />
(˜x6 +¯x6)<br />
CSC<br />
˙˜x6 = 1<br />
˙˜x7 = 1<br />
LSC<br />
LL<br />
[(˜x4 +¯x4) − (˜x7 +¯x7)<br />
+(1 − u2)(˜x6 +¯x6)]<br />
[(˜x1 +¯x1) − (˜x3 +¯x3)] (7)<br />
[(˜x5 +¯x5) − (1 − u2)(˜x3 +¯x3)]<br />
[(˜x3 +¯x3) − RL(˜x7 +¯x7) − E]<br />
The PCH form of stu<strong>di</strong>ed system with the new<br />
variable ˜x <strong>and</strong> in function of the gra<strong>di</strong>ent of the<br />
desired energy (6) is:<br />
where<br />
˙˜x =(J (u1,u2) −R) .∇Hd + Ai(¯x, u) (8)<br />
J (u1,u2) −R=<br />
⎡<br />
⎢<br />
⎣<br />
0<br />
1 − u1<br />
−<br />
CsLN 1 − u1<br />
CsL N<br />
0<br />
−1<br />
CsL DC<br />
0 0 0<br />
0 0 0 0 0 0<br />
0 0 0<br />
1<br />
CsL DC<br />
0<br />
−1<br />
C DC L DC<br />
1<br />
C DC L DC<br />
0 0 0 0 0<br />
0 0<br />
1 − u2<br />
−<br />
CDC LSC 0 0<br />
1<br />
CDC LL ⎡ ⎤<br />
Cs˜x1<br />
⎢ LN ˜x2 ⎥<br />
⎢ ⎥<br />
⎢CDC<br />
˜x3 ⎥<br />
⎢ ⎥<br />
∇Hd = ⎢LDC<br />
˜x4 ⎥<br />
⎢ ⎥<br />
⎢CSC˜x5<br />
⎥<br />
⎢ ⎥<br />
⎣LSC˜x6<br />
⎦<br />
LL˜x7<br />
0<br />
1 − u2<br />
C DC L SC<br />
−1<br />
C DCL L<br />
0 0 0 0<br />
0<br />
1<br />
C SCL SC<br />
−1<br />
C SCL SC<br />
0 0 0<br />
⎡<br />
⎤<br />
(1 − u1)¯x2 − ¯x4<br />
⎢<br />
⎥<br />
⎢ Cs ⎥<br />
⎢<br />
⎥<br />
⎢ VN − (1 − u1)¯x1<br />
⎥<br />
⎢<br />
⎥<br />
⎢ LN ⎥<br />
⎢<br />
⎥<br />
⎢ ¯x4 − ¯x7 +(1−u2)¯x6 ⎥<br />
⎢<br />
⎥<br />
⎢ CDC<br />
⎥<br />
⎢<br />
Ai(¯x, u) = ⎢ ¯x1 −<br />
⎥<br />
¯x3 ⎥<br />
⎢<br />
⎥<br />
⎢ LDC ⎥<br />
⎢<br />
⎥<br />
⎢ −¯x6 ⎥<br />
⎢<br />
⎥<br />
⎢<br />
⎥<br />
⎢ CSC ⎥<br />
⎢<br />
⎥<br />
⎢ ¯x5 − (1 − u2)¯x3 ⎥<br />
⎢<br />
⎥<br />
⎢ LSC ⎥<br />
⎢<br />
⎥<br />
⎣ ¯x3 − RL¯x7 − E ⎦<br />
LL<br />
J (u1,u2) = −J T (u1,u2) is a skew symmetric<br />
matrix defining the interconnection between the<br />
state space <strong>and</strong> R = RT ≥ 0 is symmetric positive<br />
semi definite matrix defining the damping of the<br />
system.<br />
Ai(¯x, u) evaluated at the equilibrium points (3)<br />
gives:<br />
0<br />
0 0<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 65<br />
−R L<br />
L 2 L<br />
⎤<br />
⎥<br />
⎦
⎡<br />
⎢<br />
⎣<br />
0<br />
1 − u1<br />
−<br />
CsLN ⎡<br />
⎤<br />
(E − Vd)(VN − (1 − u1)Vd)<br />
⎢<br />
⎥<br />
⎢ RLVN Cs ⎥<br />
⎢<br />
⎥<br />
⎢ VN − (1 − u1)Vd<br />
⎥<br />
⎢<br />
⎥<br />
⎢<br />
LN<br />
⎥<br />
⎢<br />
⎥<br />
⎢<br />
⎥<br />
⎢<br />
0<br />
⎥<br />
⎢<br />
⎥<br />
⎢<br />
⎥<br />
Ai = ⎢<br />
⎥<br />
⎢<br />
0<br />
⎥<br />
⎢<br />
⎥<br />
⎢<br />
⎥<br />
⎢<br />
⎥<br />
⎢<br />
0<br />
⎥<br />
⎢<br />
⎥<br />
⎢<br />
⎥<br />
⎢ ¯x5 − (1 − u2)Vd ⎥<br />
⎢<br />
⎥<br />
⎢<br />
LSC<br />
⎥<br />
⎢<br />
⎥<br />
⎣<br />
⎦<br />
0<br />
The following control laws are proposed:<br />
� u1 =ū1<br />
u2 =ū2 − r˜x6<br />
where r is a design parameter (r ≥ 0).<br />
(9)<br />
(10)<br />
Proposition 1. The origine of the closed loop PCH<br />
system (8), with the control laws (10) <strong>and</strong> (4)<br />
with the ra<strong>di</strong>ally unbounded energy function (6),<br />
is globally asymptotically stable.<br />
Proof. The closed loop dynamic of the PCH system<br />
(8) with the laws (10) <strong>and</strong> (4) with the ra<strong>di</strong>ally<br />
unbounded energy function (6) is:<br />
˙˜x =[J (u1,u2) −R ′ ] ∇Hd (11)<br />
1 − u1<br />
CsL N<br />
0<br />
where J (u1,u2) −R ′ =<br />
−1<br />
CsL DC<br />
0 0 0<br />
0 0 0 0 0 0<br />
0 0 0<br />
1<br />
CsL DC<br />
0<br />
−1<br />
C DC L DC<br />
1<br />
C DC L DC<br />
0 0 0 0 0<br />
0 0<br />
1 − u2<br />
−<br />
CDC LSC 0 0<br />
1<br />
CDC LL 0<br />
1 − u2<br />
C DCL SC<br />
−1<br />
C DC L L<br />
0 0 0 0<br />
0<br />
1<br />
C SCL SC<br />
−1<br />
C SCL SC<br />
− rV d<br />
L 2<br />
SC<br />
0 0 0<br />
0<br />
0<br />
−R L<br />
L 2<br />
L<br />
R ′ = R ′T<br />
≥ 0. The derivative of the desired<br />
energy function (6) along the trajectory of (11)<br />
is:<br />
Hd ˙ = ∇H T d ˙˜x = −∇H T d R′ ∇Hd ≤ 0 (12)<br />
5. SIMULATIONS<br />
5.1 Load works as a receiver<br />
The following simulations present the system response<br />
<strong>and</strong> control obtained with the proposed<br />
⎤<br />
⎥<br />
⎦<br />
⊳<br />
control laws (10). In this case, the load is considered<br />
as a receiver. To illustrate the controller<br />
efficiency, the DC bus voltage reference, the electromotive<br />
force (emf) <strong>and</strong> the resistance are mo<strong>di</strong>fied<br />
(see Figure 5 <strong>and</strong> Figure 6). The DC bus<br />
voltage is initialized at 36V <strong>and</strong> the DC Bus<br />
voltage reference is set at 42V at the beginning.<br />
Figure 2 presents the system response to changes<br />
in the DC Bus voltage reference (Vd), emf (E)<br />
<strong>and</strong> load current iL. The DC Bus voltage tracks<br />
well the reference, i.e. very low overshoot <strong>and</strong> no<br />
steady state error are observed.<br />
V d & V DC (V)<br />
i L (A)<br />
50<br />
45<br />
40<br />
35<br />
2.5<br />
1.5<br />
0.5<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
2<br />
1<br />
0<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 2. (a) DC Bus voltage <strong>and</strong> its reference. (b)<br />
Load current.<br />
Figure 3 shows the source voltage (VN )<strong>and</strong>current<br />
(iN ). In our modeling, we assume that the<br />
DC source is ideal, thus VN stay at constant value<br />
regardless of the current iN .Asmoothbehavior<br />
of the current is observed regar<strong>di</strong>ng the changes in<br />
Vd, E <strong>and</strong> RL, because the SC supply the transient<br />
power.<br />
V N (V)<br />
i N (A)<br />
16<br />
15.5<br />
15<br />
14.5<br />
14<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
8<br />
6<br />
4<br />
2<br />
0<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 3. (a) DC source voltage. (b) DC source<br />
current.<br />
Figure 4 shows the SC voltage <strong>and</strong> current responses.<br />
The SC supply power to the load in the<br />
transient <strong>and</strong> in the steady state no power or<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 66
energy is extracted since the current iSC is nul.<br />
The positive sens of iSC means that the SC supply<br />
the load <strong>and</strong> the negative one corresponds to the<br />
recover of energy by the SC. At time t =4s, the<br />
SC absorb the current pick to respond quickly to<br />
the fast DC reference change.<br />
V SC (V)<br />
12<br />
11.999<br />
11.998<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
i SC (A)<br />
6<br />
4<br />
2<br />
0<br />
−2<br />
−4<br />
−6<br />
0 1 2 3 4 5 6<br />
t(s)<br />
Fig. 4. (a) SC voltage. (b) SC current.<br />
Figure 5 <strong>and</strong> Figure 6 present the network Boost<br />
controller, the SC bi<strong>di</strong>rectional converter controller,<br />
the changes in the load resistance RL <strong>and</strong><br />
in emf. UN <strong>and</strong> USC are in the set [0, 1].<br />
U N<br />
U SC<br />
0.65<br />
0.6<br />
0.55<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
0.8<br />
0.75<br />
0.7<br />
0.65<br />
0.6<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 5. (a) Source Boost control. (b) SC converter<br />
control.<br />
Figure 7 presents the power transfers in the system.<br />
Power pick are absorbed or supplied by<br />
SC, thus a smooth power is provided by the DC<br />
source. This can reduce significantly the harmonics<br />
on the line.<br />
It can be seen from Figure 2 that the system with<br />
the proposed controller is robust towards load<br />
resistance changes <strong>and</strong> emf variations.<br />
5.2 Load works as a generator<br />
The following simulations present the system response<br />
when the load is considered as a generator.<br />
R L (Ω)<br />
E(V)<br />
11<br />
10<br />
9<br />
8<br />
7<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
30<br />
25<br />
20<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 6. (a) Load resistance change. (b) Load emf<br />
change.<br />
Power (W)<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
−20<br />
−40<br />
Load<br />
DC source<br />
SC<br />
SC charge<br />
SC <strong>di</strong>scharge<br />
−60<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 7. Power transfers<br />
So, the proposed control laws can be tested during<br />
recovery mode (between t=1s <strong>and</strong> t=4), only the<br />
electromotive force (emf) is mo<strong>di</strong>fied for these<br />
simulations. The DC bus voltage is initialized at<br />
36V <strong>and</strong> the DC Bus voltage reference is set at<br />
42V.<br />
Figure 8 presents the system response to changes<br />
in the emf (E). The DC Bus voltage tracks well<br />
the reference during the first second, then a small<br />
overshoot <strong>and</strong> a steady state error are observed<br />
when the load current becomes negative. This is a<br />
7% error which is acceptable in most of the case,<br />
an improvement will be presented in section 6 to<br />
cancel this error.<br />
Figure 9 shows the source voltage <strong>and</strong> current. VN<br />
stay at constant value, as it is explained in the<br />
last simulations (5.1). A smooth behavior of the<br />
current is observed regar<strong>di</strong>ng the changes in E,<br />
this is because the SC supply the transient power.<br />
When the load provides energy, all goes to the<br />
SC because the DC-DC source converter is not<br />
reversible.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 67
V d & V DC (V)<br />
i L (A)<br />
50<br />
45<br />
40<br />
35<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
3<br />
2<br />
1<br />
0<br />
−1<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 8. (a) DC Bus voltage <strong>and</strong> its reference. (b)<br />
Load current.<br />
V N (V)<br />
i N (A)<br />
16<br />
15.5<br />
15<br />
14.5<br />
14<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
8<br />
6<br />
4<br />
2<br />
0<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 9. (a) DC source voltage. (b) DC source<br />
current.<br />
All the current provided by the load is absorbed<br />
by the SC during the recovery mode, as shown<br />
Figure 10. The SC supply power to the load in<br />
the transient like it was shown in section 5.1. The<br />
SC voltage increase when the load works as a<br />
generator.<br />
V SC (V)<br />
12.015<br />
12.01<br />
12.005<br />
12<br />
11.995<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
i SC (A)<br />
5<br />
0<br />
−5<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 10. (a) SC voltage. (b) SC current.<br />
Figure 11 shows the emf changes <strong>and</strong> the control<br />
signals of the converters.<br />
U N<br />
U SC<br />
E(V)<br />
0.8<br />
0.7<br />
0.6<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
0.8<br />
0.7<br />
0.6<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
50<br />
40<br />
30<br />
20<br />
0 1 2 3 4 5 6<br />
t(s)<br />
Fig. 11. (a) Source Boost control. (b) SC converter<br />
control. (c) Load emf change.<br />
Figure 12 presents the power transfers in the<br />
system. As in Figure 7, power pick are absorbed or<br />
supplied by SC, so a smooth power is provided by<br />
the DC source. During the energy recovery, all the<br />
power coming from the load goes to the SC <strong>and</strong><br />
the DC source provides a very low power (due to<br />
the source converter model).<br />
Power (W)<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
−20<br />
DC source<br />
Load<br />
−40<br />
SC<br />
−60<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 12. Power transfers<br />
The system behaviour follows requirements developed<br />
in section 3.<br />
6.1 New control<br />
6. IMPROVEMENT<br />
In the last solution, only one measure (iSC) was<br />
done. In order to cancel the steady state error on<br />
the DC bus voltage, a integrator can be added. DC<br />
bus voltage (VDC) has to be known so its measure<br />
is necessary. The integrator action is added in the<br />
control equation u2 (10) <strong>and</strong> allows to reduce the<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 68
error between VDC <strong>and</strong> Vd. So the new control<br />
laws are:<br />
⎧⎨<br />
u1 =ū1<br />
�<br />
⎩ u2 =ū2 − r˜x6 − Ki ˜x3<br />
(13)<br />
The stability proof is given in [8]. Since the<br />
close loop system is stable, the ad<strong>di</strong>tion of an<br />
intergrator do not mo<strong>di</strong>fy the stability. In the next<br />
part, the results are presented.<br />
6.2 Simulations<br />
For the simulations, the same configuration as in<br />
5.2 is chosen, new control (13) is applied.<br />
Figure 13 presents the system response to changes<br />
in the emf (E). The steady state error is cancelled<br />
with this new control but there is still an<br />
overshoot around 8V. The current value is very<br />
similar to the one Figure 8, except during the<br />
recovery mode. Its value is <strong>di</strong>fferent because DC<br />
bus voltage is maintained at 42V.<br />
V d & V DC (V)<br />
i L (A)<br />
50<br />
45<br />
40<br />
35<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
3<br />
2<br />
1<br />
0<br />
−1<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 13. (a) DC Bus voltage <strong>and</strong> its reference. (b)<br />
Load current.<br />
As shown Figure 14, during the energy recovery,<br />
the DC source current goes close to zero because<br />
the DC-DC converter is not reversible. A small<br />
overshoot of the current is observed when the DC<br />
source start to provide energy to the system (at<br />
t=0s <strong>and</strong> t=4s).<br />
Figure 15, the SC still provide transients, but do<br />
not go to zero during steady state. This is due to<br />
the new term in the control equation 13. So when<br />
the load absorbs energy, the DC source <strong>and</strong> the<br />
SC provide it together.<br />
The same thing can be underline on Figure 16,<br />
the load power is the sum of SC <strong>and</strong> DC source<br />
power during steady state.<br />
Figure 17 shows the emf changes <strong>and</strong> the control<br />
signals of the converters.<br />
V N (V)<br />
i N (A)<br />
16<br />
15.5<br />
15<br />
14.5<br />
14<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
8<br />
6<br />
4<br />
2<br />
0<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 14. (a) DC source voltage. (b) DC source<br />
current.<br />
V SC (V)<br />
12.02<br />
12.01<br />
12<br />
11.99<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
i SC (A)<br />
5<br />
0<br />
−5<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 15. (a) SC voltage. (b) SC current.<br />
Power (W)<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
−20<br />
Load<br />
DC source<br />
−40<br />
SC<br />
−60<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 16. Power transfers<br />
7. CONCLUSION<br />
A modeling of hybrid sources system composed<br />
of a DC energy source <strong>and</strong> SC power source is<br />
presented. PCH structure of the overall system is<br />
given exhibiting important physical properties in<br />
terms of variable interconnection <strong>and</strong> damping of<br />
the system. The problem of the DC Bus Voltage<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 69
U N<br />
U SC<br />
E(V)<br />
0.8<br />
0.7<br />
0.6<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
0.8<br />
0.7<br />
0.6<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
50<br />
40<br />
30<br />
20<br />
0 1 2 3<br />
t(s)<br />
4 5 6<br />
Fig. 17. (a) Source Boost control. (b) SC converter<br />
control. (c) Load emf change.<br />
control is solved using simple linear controller<br />
based on an IDA-PBC approach.<br />
An important property has to be underline, only<br />
iSC measure is needed for the first controller (10).<br />
Global stability proof is given <strong>and</strong> encouraging<br />
simulation results has been obtained. Many benefits<br />
can be expected from the proposed structure<br />
such that supplying <strong>and</strong> absorbing the power picks<br />
by using SC which also allow recovering energy. At<br />
the same time, this can reduce significantly the<br />
harmonics on the line.<br />
Finally, two sensors (instead of one) are used to<br />
cancelled the steady state error with an integrator<br />
(13). Thus depen<strong>di</strong>ng of the application requirements,<br />
a solution with one sensor can be chosen<br />
or a second solution with two sensors.<br />
REFERENCES<br />
[1] A.J van der Schaft, B.M. Maschke, “On the<br />
hamiltonian formulation of nonholonomic mechanical<br />
systems”, Reports on Mathematical<br />
Physics, vol.34, no.2, pp.225-233, 1994.<br />
[2] R. Ortega, A. Loria, P.J. Nicklasson, <strong>and</strong><br />
H. Sira-Ramirez, “Passivity-based control<br />
of Euler-Lagrange systems,” in Communications<br />
<strong>and</strong> Control Engineering. Berlin,<br />
Germany:Spring-Verlag, 1998.<br />
[3] R. Ortega, A.J van der Schaft, B. Maschke<br />
<strong>and</strong> G. Escobar, “Interconnection <strong>and</strong> damping<br />
assignment passivity-based control of portcontrolled<br />
hamiltonian systems,” Automatica<br />
vol.38, pp.585-596, 2002.<br />
[4] M. Becherif <strong>and</strong> E. Mendes, “Stability <strong>and</strong><br />
robustness Disturbed-Port Controlled Hamiltonian<br />
system with Dissipation,” 16th IFAC<br />
World Congress, Prague ,2005,<br />
[5] S.M. Halpin <strong>and</strong> S.R. Ashcraft, “Design considerations<br />
for single-phase uninterruptible<br />
power supply using double-layer capacitors as<br />
the energy storage element” IEEE-IAS, San<br />
Diego, 1996, v4, pp 2396–2403<br />
[6] M. Becherif, M.Y. Ayad <strong>and</strong> A. Miraoui,<br />
“Modeling <strong>and</strong> Passivity-Based Control of <strong>Hybrid</strong><br />
Sources: Fuel Cell <strong>and</strong> Supercapacitors”<br />
41 st IEEE-IAS, 2006<br />
[7] M. Becherif, “Passivity-Based Control of <strong>Hybrid</strong><br />
Sources: Fuel Cell <strong>and</strong> battery” 11 th IFAC<br />
Symposium on Control in Transportation systems,<br />
2006<br />
[8] R. Ortega <strong>and</strong> E. Garcia-Canseco, “Interconnection<br />
<strong>and</strong> Damping Assignment Passivity-<br />
Based Control: A Survey”, European Journal<br />
of Control, 2004<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 70
HYBRID ELECTRIC VEHICLES :<br />
FROM OPTIMIZATION TOWARD REAL-TIME<br />
CONTROL STRATEGIES<br />
Gregory Rousseau ∗,∗∗ Delphine Sinoquet ∗<br />
Pierre Rouchon ∗∗<br />
∗ Institut français du pétrole, 1 et 4, avenue de Bois-Préau,<br />
92852 Rueil-Malmaison Cedex - France<br />
∗∗ Ecole des Mines de Paris<br />
Abstract: <strong>Hybrid</strong>-electric vehicles appear to be one of the most promising technologies<br />
for reducing fuel consumption <strong>and</strong> pollutant emissions. The presented<br />
work focuses on two types of architecture : a mild hybrid <strong>and</strong> a full hybrid where<br />
the kinetic energy in the breaking phases is stored in a battery to be re-used<br />
later via the electric motor. This ad<strong>di</strong>tional traction power allows to downsize<br />
the engine <strong>and</strong> still fulfill the power requirements. Moreover, the engine can be<br />
turned off in idle phases for both architectures <strong>and</strong> for the parallel architecture,<br />
it may be turned off whereas the electric motor furnishes all the traction power.<br />
The optimal control problem of the energy management between the two power<br />
sources is solved for given driving cycles by a classical dynamic programming<br />
method <strong>and</strong> by an alternative method based on Pontryagin Minimum Principle.<br />
The real time control laws to be implemented on the vehicle are derived from the<br />
resulting optimal control strategies. These control laws are evaluated on another<br />
driving cycle which was not given a priori.<br />
Keywords: <strong>Hybrid</strong> vehicle, Optimal control, Dynamic programming, Pontryagin,<br />
Control strategies<br />
1. INTRODUCTION<br />
Growing environmental concerns coupled with<br />
concerns about global crude oil supplies stimulate<br />
research on new vehicle technologies. <strong>Hybrid</strong>electric<br />
vehicles appear to be one of the most<br />
promising technologies for reducing fuel consumption<br />
<strong>and</strong> pollutant emissions (German, 2003) :<br />
mainly thanks to the system stop’n go that allows<br />
to turn off the engine in idle phases, to the recuperated<br />
braking energy to be stored in a battery<br />
<strong>and</strong> re-used later via the electric motor <strong>and</strong> to the<br />
possibility to downsize the engine.<br />
The energy management of hybrid power trains<br />
requires then some specific control laws : they rely<br />
on the estimation of the battery state of charge<br />
which provides the remaining level of energy, <strong>and</strong><br />
the variable efficiency of each element of the power<br />
train has to be taken into account. Optimization<br />
of energy management strategies on given driving<br />
cycles is often used to derive sub-optimal control<br />
laws to be implemented on the vehicle (see among<br />
others (Sciarretta et al., 2004), (Scor<strong>di</strong>a, 2004),<br />
(Wu et al., 2002), (Delprat, 2002)).<br />
IFP, in partnership with Gaz de France <strong>and</strong> the<br />
Ademe, has combined its downsizing technology<br />
with a natural gas engine in a small urban demonstrator<br />
vehicle (VEHGAN vehicle), equipped with<br />
a starter alternator <strong>and</strong> supercapacitor manufactured<br />
by Valeo (Tilagone <strong>and</strong> Venturi, 2004).<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 71
In this paper, we present two <strong>di</strong>fferent optimization<br />
algorithms <strong>and</strong> apply them to a simplified<br />
model of the VEHGAN vehicle <strong>and</strong> to a parallel<br />
architecture version of this vehicle: a classical Dynamic<br />
Programming algorithm ((Wu et al., 2002),<br />
(Scor<strong>di</strong>a, 2004), (Sciarretta et al., 2004)), <strong>and</strong> an<br />
original algorithm based on Pontryagin Minimum<br />
Principle that allows to h<strong>and</strong>le constraints on the<br />
state <strong>and</strong> control variables. Finally, we propose<br />
two types of control strategies derived from the<br />
optimization results on given driving cycles <strong>and</strong><br />
evaluate them as a real time strategy on a driving<br />
cycle which was not given a priori.<br />
2. SYSTEM MODELLING AND OPTIMAL<br />
CONTROL PROBLEM<br />
2.1 Characteristics of the considered hybrid vehicle<br />
Two <strong>di</strong>fferent architectures are modelled:<br />
• a mild hybrid architecture : the engine can<br />
not be stopped when the requested torque is<br />
provided only by the electric motor, except<br />
for the stop’n go mode at the idle speed.<br />
So, for a control that cancels the engine<br />
torque <strong>and</strong> for positive torque request, the<br />
fuel consumption does not vanish (Figure 1),<br />
• a full parallel hybrid architecture : the engine<br />
can be stopped to let the electric motor<br />
power alone the vehicle. In that case, the fuel<br />
consumption vanishes.<br />
In both cases, the battery is regenerated in braking<br />
phases accor<strong>di</strong>ngly to the available minimum<br />
electric torque at the considered engine speed.<br />
In order to solve the optimal control problem of<br />
energy management, we build a simplified model<br />
which is composed of :<br />
• a driving cycle to be followed (imposing vehicle<br />
speed <strong>and</strong> gear shifts),<br />
• a vehicle model defining its mass, wheel inertia,<br />
resistance force,<br />
• a manual gearbox with 5 gear ratios,<br />
• a 660CC natural gas engine characterized by<br />
a fuel consumption map <strong>di</strong>splayed in Figure 1<br />
<strong>and</strong> a maximum torque depen<strong>di</strong>ng on the<br />
engine speed (see (5)),<br />
• a starter alternator (3kW for mild-hybrid,<br />
6kW for full-hybrid) characterized by a maximum<br />
torque <strong>and</strong> a minimum torque for regenerative<br />
braking phases, both depen<strong>di</strong>ng<br />
on the engine speed (see (6)). Its efficiency is<br />
assumed to be 1 in the presented examples,<br />
• a battery characterized by a capacity of<br />
0.4Ah for mild-hybrid architecture <strong>and</strong> 40Ah<br />
for full-hybrid one. The variations of the battery<br />
state of charge are modelled by<br />
Engine Torque (N.m)<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0.24 0.18 0.19 0.25<br />
0.22 0.21 0.23 0.2<br />
0.28<br />
0.29<br />
0.3<br />
0.26<br />
0.27<br />
0.31<br />
0.24<br />
0.22 0.21 0.23 0.2 0.18 0.19<br />
0.25<br />
0.28<br />
0.29<br />
0.3<br />
0.26<br />
0.27<br />
0.31<br />
0.22 0.21 0.23 0.2 0.18 0.19<br />
0.24<br />
0.32<br />
0.34<br />
0.25<br />
0.28<br />
0.29<br />
0.26<br />
0.27<br />
0.21<br />
0.22<br />
0.24<br />
0.23<br />
0<br />
1000 2000 3000 4000 5000 6000<br />
Engine Speed (rpm)<br />
0.3<br />
0.31<br />
0.18<br />
0.2<br />
0.19<br />
0.25<br />
0.26<br />
0.27<br />
0.28<br />
0.28<br />
0.24 0.22 0.21 0.23 0.2 0.18 0.19<br />
0.29<br />
0.3<br />
0.29<br />
0.3<br />
0.32<br />
0.33<br />
0.34<br />
0.25 0.27 0.26<br />
0.310.31<br />
0.32<br />
0.37<br />
0.37<br />
0.42<br />
0.45<br />
0.45 0.43 0.44<br />
0.33<br />
0.32<br />
0.33<br />
0.360.29<br />
0.28 0.32 0.24 0.22 0.21 0.23 0.37 0.2 0.18 0.19 0.38 0.34 0.3<br />
0.43<br />
0.33<br />
0.33<br />
0.35<br />
0.36 0.35<br />
0.36<br />
0.39<br />
0.37<br />
0.4 0.38<br />
0.42<br />
0.34<br />
0.39<br />
0.38<br />
0.44<br />
0.350.45<br />
0.4<br />
0.43<br />
0.35<br />
0.41<br />
0.36<br />
0.43 0.44 0.42<br />
0.41 0.40.39<br />
0.38<br />
0.45<br />
0.34<br />
0.35<br />
0.38<br />
0.39<br />
0.36<br />
0.39<br />
0.4<br />
0.41<br />
0.4 0.42<br />
0.41<br />
0.43<br />
0.44<br />
0.41 0.42<br />
Fig. 1. Fuel consumption map of natural gas<br />
engine of VEHGAN vehicle<br />
′ ω(t)Tm(t)K<br />
˙x(t) = −<br />
Ubattncapa<br />
0.44<br />
0.37<br />
0.45<br />
(1)<br />
with ω(t), the electric motor <strong>and</strong> engine<br />
speed (assumed to be equal), Ubatt, the battery<br />
voltage considered to be constant, K ′ ,<br />
a scaling constant <strong>and</strong> ncapa, the nominal<br />
capacity of the battery.<br />
The driving cycle is converted in a (engine speed,<br />
torque) trajectory either thanks to a backward<br />
model based on the vehicle model, or thanks to a<br />
forward model as in AMESim Drive library which<br />
furnishes a more realistic trajectory taking into<br />
account a simulated behavior of a driver as the<br />
anticipation of the driving cycle.<br />
2.2 Optimal Control Problem<br />
The optimal control problem under study consists<br />
in minimizing the fuel consumption of the vehicle<br />
along a given driving vehicle cycle, taking into<br />
account physical constraints from battery, engine<br />
<strong>and</strong> electric motor. The control variable associated<br />
with this problem is called u(t). It represents<br />
the <strong>di</strong>stribution of the requested torque Trq, between<br />
the engine torque Te <strong>and</strong> the electric motor<br />
torque Tm, written as<br />
⎧<br />
⎨Trq(t)<br />
= Te(t) + Tm(t)<br />
Te(t) = u(t)Trq(t)<br />
(2)<br />
⎩<br />
Tm(t) = (1 − u(t))Trq(t).<br />
The state variable is the battery state of charge<br />
x(t) <strong>and</strong> follows from (1)<br />
˙x(t) = −Kω(t)(1 − u(t))Trq(t) = f(u(t),t), (3)<br />
where K = K ′<br />
Ubattncapa .<br />
The resulting optimization problem is then the<br />
following :<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 72
⎧ ⎧<br />
⎫<br />
⎨ �T<br />
⎬<br />
⎪⎨<br />
min J(u) = L(u(t),t)dt + g(x(T),T)<br />
u ⎩ ⎭<br />
0<br />
subject to : ˙x = f(u(t),t),<br />
⎪⎩<br />
xmin ≤ x(t) ≤ xmax<br />
umin(t) ≤ u(t) ≤ umax(t)<br />
x(0) = x0<br />
(4)<br />
with 0 <strong>and</strong> T, respectively the initial <strong>and</strong> the<br />
final times of the given driving cycle, L(u(t),t),<br />
the instantaneous fuel consumption, computed<br />
from the map <strong>di</strong>splayed in Figure 1, g(x(T),T),<br />
the penalization term that constrains the final<br />
state of charge to be close to the initial state of<br />
charge in order to maintain a null electrical energy<br />
balance (to avoid to <strong>di</strong>scharge totally the battery<br />
for minimizing the consumption).<br />
The bound constraints on the state <strong>and</strong> on the<br />
control in (4) are derived from the following constraints<br />
:<br />
• the engine can only produce a positive<br />
torque, <strong>and</strong> is limited to a maximum torque<br />
which depends on engine speed ω(t), written<br />
as 0 ≤ Te(t) ≤ T max<br />
e (ω(t)), <strong>and</strong> leads to<br />
0 ≤ u(t)Trq(t) ≤ T max<br />
e (ω(t)), (5)<br />
• the electric motor torque is limited between<br />
a maximum torque <strong>and</strong> a minimum torque<br />
during regenerating breaking, T min<br />
m (ω(t)) ≤<br />
Tm(t) ≤ T max<br />
m (ω(t)), <strong>and</strong> leads to the control<br />
constraints<br />
T min<br />
m (ω(t)) ≤ (1 − u(t))Trq(t) ≤ T max<br />
m (ω(t)),(6)<br />
• the storage capacity implies a minimum <strong>and</strong><br />
a maximum state of charge of the battery<br />
(which are fixed to 0% <strong>and</strong> 100% in our<br />
example)<br />
xmin ≤ x(t) ≤ xmax. (7)<br />
In this optimal control problem, we make several<br />
assumptions<br />
• the pollutant emissions are not taken into<br />
account in the optimization process,<br />
• the engine speed <strong>and</strong> the electric motor speed<br />
are equal,<br />
• in the mild hybrid case, recharging the battery<br />
is only possible for negative torques<br />
(breaking request), we <strong>di</strong>d not consider regeneration<br />
by an ad<strong>di</strong>tional engine torque<br />
beyond the driver request torque. Thus the<br />
control u(t) remains between 0 <strong>and</strong> 1. In the<br />
full hybrid case, u(t) can take values larger<br />
than 1, allowing battery regeneration with<br />
ad<strong>di</strong>tional engine torque.<br />
In the following, we will call U(t) in continuous<br />
time (respectively Uk in <strong>di</strong>screte time) the feasible<br />
domain for u(t) (respectively uk) with respect to<br />
the constraints (5) <strong>and</strong> (6).<br />
3. DYNAMIC PROGRAMMING<br />
OPTIMIZATION<br />
The Dynamic Programming method (DP) is classically<br />
used to solve the problem (4) ((Wu et<br />
al., 2002), (Scor<strong>di</strong>a, 2004)) : it relies on the principle<br />
of optimality or Bellman principle. First, the<br />
optimal control problem (4) is <strong>di</strong>scretized in time<br />
⎧<br />
N−1 �<br />
⎪⎨ min J(u) := Lk(uk) + g(xN)<br />
uk∈Uk<br />
k=0<br />
(8)<br />
⎪⎩<br />
subject to : xk+1 = fk(xk,uk), x(0) = x0<br />
xmin ≤ xk ≤ xmax<br />
where Lk(uk) is the cumulated fuel consumption<br />
over the time interval [k,k + 1], xk is the state<br />
of charge of the battery at time k, fk is the<br />
function that modelizes the battery state of charge<br />
evolution in the <strong>di</strong>screte form of (3) <strong>and</strong> g(xN) =<br />
β.(xN − x0) 2 is the penalization term for the<br />
constraint on final state of charge (β is a constant<br />
to be chosen 1 ), N being the final time of the<br />
driving cycle.<br />
From Bellman principle, the minimum cost Vk(xk)<br />
at the time step k, 0 ≤ k ≤ N − 1, is expressed as<br />
Vk(xk) = min (Lk(uk) + Vk+1(fk(uk))). (9)<br />
uk∈Uk<br />
At time N, the cost function is VN(xN) = g(xN).<br />
This optimization problem is solved backward<br />
from final time step to initial time step using a<br />
<strong>di</strong>scretization of function V in the control space<br />
<strong>and</strong> in the state space.<br />
3.1 DP Optimization algorithm<br />
A st<strong>and</strong>ard time step used in our examples is 1s,<br />
<strong>and</strong> the step for state <strong>di</strong>scretization is 0.5%. Two<br />
algorithms may be used to solve the DP problem :<br />
• a classical DP algorithm, called Ford algorithm<br />
in the following (Scor<strong>di</strong>a, 2004), consists<br />
in exploring all the feasible controls (to<br />
go from a point xi k to an other point xj<br />
k+1 ),<br />
finally taking the best trajectory (the trajectory<br />
which minimizes at each step k the sum<br />
Lk(uk) + Vk+1(fk(uk))). In such a method,<br />
the state of charge trajectory remains on the<br />
points of the defined grid in the state space<br />
which may lead to inaccurate results.<br />
• the chosen algorithm interpolates the function<br />
V (xk,k) in the state space, for each<br />
time step k thanks to an upwind scheme<br />
(Guilbaud, 2002) :<br />
1 In the following results, a value depen<strong>di</strong>ng of battery<br />
capacity has been implemented<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 73
Torque (Nm) <strong>and</strong> Speed (m/s)<br />
State of charge (%)<br />
100<br />
80<br />
60<br />
40<br />
20<br />
Vehicle speed <strong>and</strong> requested torque<br />
0<br />
0 100 200 300 400 500<br />
Time<br />
600 700 800 900 1000<br />
State of charge trajectories<br />
100<br />
80<br />
60<br />
40<br />
Requested torque (Nm)<br />
Vehicle speed (m/s)<br />
20<br />
Upwind scheme with dX=2.5% − CPU Time 86s<br />
Upwind scheme with dX=0.5% − CPU Time 354s<br />
0<br />
Ford algo with dX=2.5% − CPU Time 18s<br />
Ford algo with dX=0.5% − CPU Time 197s<br />
−20<br />
0 100 200 300 400 500<br />
Time (s)<br />
600<br />
PMP algorithm − CPU Time 3s<br />
700 800 900 1000<br />
Fig. 2. Urban Artemis cycle (Top); Optimal state of charge trajectory of VEHGAN vehicle computed<br />
with PMP & DP algorithm (Bottom).<br />
Vk(x i k) = min [∆tLk(uk) + Vk+1(x<br />
uk∈Uk<br />
i k+1)<br />
+fk(uk) Vk+1(xi i−1<br />
k+1 ) − Vk+1(xk+1 )<br />
∆x<br />
∆t], (10)<br />
where ∆x <strong>and</strong> ∆t are respectively the state<br />
<strong>and</strong> the time <strong>di</strong>scretization step size. We refer<br />
to (Guilbaud, 2002) for some theoretical results<br />
on the convergence of this method <strong>and</strong><br />
error estimations. Therefore, it is possible<br />
to use a (state) continuous constrained optimization<br />
algorithm to solve each problem (9)<br />
which should furnish more accurate results<br />
than Ford algorithm. Nevertheless, this algorithm<br />
is generally more expensive in terms of<br />
computing time.<br />
These two optimization algorithms are only used<br />
when Trq > 0 : when the requested torque is<br />
negative, the optimal control uk is completely<br />
known, as the battery is regenerated as much as<br />
possible, the control uk being constrained by the<br />
minimal electric motor torque from (6) <strong>and</strong> by<br />
maximum SOC from (7).<br />
Optimization results obtained with DP method<br />
are <strong>di</strong>splayed on Figure 2.<br />
4. PONTRYAGIN MINIMUM PRINCIPLE<br />
OPTIMIZATION<br />
In this section, we propose an alternative method<br />
to solve the optimal control problem (4). It relies<br />
on the Pontryagin Minimum Principle (PMP)<br />
<strong>and</strong> unlike the DP method does not require any<br />
<strong>di</strong>scretization scheme.<br />
4.1 Pontryagin Minimum Principle<br />
First we consider the optimization problem (4)<br />
<strong>and</strong> introduce the Hamiltonian function, without<br />
considering state <strong>and</strong> control constraints<br />
H(u(t),x(t),p(t)) = L(u(t),t) + p(t) ˙x(t). (11)<br />
p(t) is called the co-state of our system. We<br />
assume here that L is a smooth convex function<br />
of u.<br />
The Pontryagin Minimum Principle states the<br />
following con<strong>di</strong>tions for the unconstrained optimal<br />
control problem :<br />
∂H<br />
∂x<br />
= − ˙p <strong>and</strong><br />
∂H<br />
∂u<br />
= 0. (12)<br />
We refer to (Pontryagin et al., 1974) <strong>and</strong> (Bryson<br />
<strong>and</strong> Ho, 1975) for further details about Pontryagin<br />
Principle.<br />
4.2 Application<br />
The fuel consumption L(u(t),t) to be minimized<br />
in (4), is defined by a <strong>di</strong>screte map L(ω,Te), mod-<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 74
elled by a 2-order polynomial, which is represented<br />
as<br />
L(ω,Te) =<br />
2�<br />
Kijω i T j e , (13)<br />
i,j=0<br />
which allows to model a large variety of engine<br />
maps (Rousseau et al., 2006).<br />
4.2.1. Mild-<strong>Hybrid</strong> case In the mild-hybrid vehicle<br />
case, the fuel consumption can not be cancelled.<br />
We do not consider the stop <strong>and</strong> start, as<br />
well as the possibility to power the vehicle only<br />
with the electric motor.<br />
From (12) <strong>and</strong> (3) we obtain<br />
˙p = 0 ⇒ p = constant = p0. (14)<br />
Without any constraint on the state <strong>and</strong> on the<br />
control, the problem of minimizing H can be easily<br />
solved. The minimum fuel consumption is then<br />
reached for u ∗ so as<br />
∂H<br />
∂u<br />
∂L<br />
= + p∂f = 0. (15)<br />
∂u ∂u<br />
The optimal control u ∗ can be calculated easily by<br />
solving the equation (15), which depends linearly<br />
on u (thanks to (3) <strong>and</strong> (13)) . u ∗ finally depends<br />
on p(t), Trq(t) <strong>and</strong> ω(t)<br />
u ∗ (t) = −<br />
2�<br />
Ki1ω(t) i + p0.K.ω(t)<br />
i=0<br />
2<br />
2�<br />
Ki2ω(t) i .Trq(t)<br />
i=0<br />
. (16)<br />
The expression of p0 is obtained by replacing<br />
u ∗ (t) by its expression in the state equation (3),<br />
<strong>and</strong> by integrating this equation in time, between<br />
Tinit <strong>and</strong> τ, Tinit <strong>and</strong> τ being respectively the<br />
considered initial <strong>and</strong> final times.<br />
4.2.2. Full-<strong>Hybrid</strong> case With the full-hybrid<br />
case, we have to consider the possibility to power<br />
the vehicle only with the electric motor. The<br />
previous expression of Hamiltonian becomes unadapted,<br />
as the fuel consumption can be completely<br />
cancelled. The fuel consumption function<br />
is then <strong>di</strong>scontinuous<br />
Lfh(ω(t),Te(t)) =<br />
� 0 if u(t) = 0<br />
L(ω(t),Te(t)) if u(t) �= 0. (17)<br />
The Hamiltonian, in the only electric motor case<br />
(u(t) = 0), is then written<br />
Hm(x(t),p(t)) = p(t) ˙x(t). (18)<br />
The optimal control u ∗ must then be written as<br />
u ∗ = argmin[H(u(t),x(t),p(t)), Hm(x(t),p(t))].(19)<br />
4.2.3. H<strong>and</strong>ling constraints on control <strong>and</strong> state<br />
variables The previous section presents the<br />
computation of the optimal control of the continuous<br />
problem in a restricted case where no<br />
constraint is introduced. While control constraints<br />
are generally easily taken into account, h<strong>and</strong>ling<br />
the state constraints in the continuous optimal<br />
control problem is cumbersome: several singular<br />
cases can be found in (Bryson <strong>and</strong> Ho, 1975).<br />
In our application, we are not able to find an<br />
analytic solution of the optimal control problem<br />
with control constraints : indeed, these constraints<br />
depends on time <strong>and</strong> depends on p0 which depends<br />
on final SOC (cf. previous section). By an iterative<br />
method (called algo1 in the following), we can<br />
compute the value of p0 in order to reach the<br />
desired SOC at final time with the control, expression<br />
(16), projected on its bound constraints.<br />
(Hartl et al., 1995), (Pontryagin et al., 1974),<br />
(Evans, 2000), (Bryson <strong>and</strong> Ho, 1975), (Guilbaud,<br />
2002) have stu<strong>di</strong>ed the general problem (4) with<br />
the state constraints. In our application, we can<br />
show that p(t) presents <strong>di</strong>scontinuities at the<br />
time steps where the state inequality constraints<br />
are saturated. These time steps are not a priori<br />
known : this prevents us to solve explicitly the<br />
continuous optimal control problem with these<br />
state constraints.<br />
4.2.4. PMP Optimization algorithm Considering<br />
the <strong>di</strong>fficulties described in previous section,<br />
we propose a heuristic iterative method that allows<br />
to find a sub-optimal trajectory from the<br />
constrained continuous optimal control problem<br />
(4). The proposed algorithm consists in an initialization<br />
step <strong>and</strong> 3 steps :<br />
(0) algo1 is applied on the driving cycle [0,T]<br />
(see Figure 3 Step 0). The obtained optimal<br />
trajectory violates the state constraints, the<br />
farthest SOC (ie the ”most violated point”)<br />
from the bounds being for instance at point<br />
(x(tv) = −37%,tv = 818s). The initial time<br />
is called ti, here set to 0.<br />
(1) The SOC at tv is projected on the nearest<br />
bound of the feasible state domain (for instance,<br />
SOC is fixed to xmin = 0 at point<br />
tv).<br />
(2) algo1 is applied again on [ti,tv] (see Figure 3<br />
Step 2). If the obtained trajectory still violates<br />
the state constraints on [ti,tv], steps 1<br />
<strong>and</strong> 2 are applied again on the farthest SOC<br />
from the bounds (defining a new point tv).<br />
This procedure is repeated until the trajectory<br />
remains on the feasible domain. Then<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 75
SOC<br />
SOC<br />
80<br />
60<br />
40<br />
20<br />
0<br />
−20<br />
Step 0<br />
−40<br />
0 200 400 600 800 1000 1200<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
−20<br />
Time<br />
Step 1 & 2<br />
−40<br />
0 200 400 600 800 1000 1200<br />
Time<br />
SOC<br />
SOC<br />
100<br />
80<br />
60<br />
40<br />
20<br />
Step 3<br />
0<br />
0 200 400 600 800 1000 1200<br />
100<br />
80<br />
60<br />
40<br />
20<br />
Time<br />
Final trajectory<br />
0<br />
0 200 400 600<br />
Time<br />
800 1000 1200<br />
Fig. 3. The proposed algorithm based on Pontryagin<br />
Minimum Principle.<br />
the last point tv becomes the new initial time<br />
ti in step 3.<br />
(3) algo1 is applied on [ti,T] (see Figure 3 Step<br />
3). If the obtained optimal trajectory still<br />
violates the state constraints, steps 1 <strong>and</strong> 2<br />
are repeated. This sequence is repeated until<br />
we reach the final step T at the desired final<br />
SOC, without violating the state constraints<br />
(Figure 3 bottom right).<br />
4.3 Some optimization results<br />
4.3.1. Mild <strong>Hybrid</strong> case We can compare the<br />
two optimization algorithms (DP <strong>and</strong> PMP) on<br />
the Urban Artemis driving cycle (André, 2004),<br />
in the mild <strong>Hybrid</strong> case, on Figure 2. The curves<br />
are very similar; we can notice that smaller is the<br />
state step size, nearer to the PMP curve are the<br />
DP curves.<br />
Figure 4 presents the operating points (OP) of the<br />
engine obtained with PMP algorithm.<br />
In this vehicle configuration, the state constraints<br />
are active 5 times, giving 6 <strong>di</strong>fferent values of the<br />
Lagrange multiplier p(t). We <strong>di</strong>splay the six curves<br />
(green lines) ∂H (p) = 0, which give optimal en-<br />
∂Te<br />
gine torque, function of engine speed. The engine<br />
OP are thus moved toward the green optimal<br />
curves when it is possible: the OP located below<br />
the curves remain unchanged (no battery regeneration<br />
being possible for positive torque requests<br />
for mild hybrid) whereas the OP located above are<br />
moved toward the curves by decreasing the engine<br />
torque as much as possible (saturating electric<br />
motor torque constraints).<br />
4.3.2. Full <strong>Hybrid</strong> case Figure 5 gives optimized<br />
operating points for the engine <strong>and</strong> the electric<br />
motor (PMP algorithm is used). In ad<strong>di</strong>tion to<br />
kinetic energy, we assume that it is possible to<br />
recharge the battery by using the engine at better<br />
OP, with an ideal efficiency of 1.<br />
As for mild-hybrid case, the optimal trajectory<br />
(continuous green line) gives the optimal operating<br />
points of the engine by fin<strong>di</strong>ng the solution of<br />
∂H = 0. Thus, many of low torque OP are moved<br />
∂Te<br />
to the optimal trajectory, recharging the battery<br />
by imposing a negative electric motor torque. As<br />
the full-hybrid configuration allows to turn off<br />
the engine for non-zero vehicle speed (pure electric<br />
mode), most of OP associated with engine<br />
speed below 3000 rpm <strong>and</strong> requested torque below<br />
20Nm, lead to turn off the engine (points where<br />
engine torque is zero) : turning off the engine<br />
is more efficient than the optimal engine torque<br />
(green curve : ∂H = 0). ∂Te<br />
5. REAL-TIME CONTROL<br />
From optimization results on Urban Artemis cycle,<br />
we derive suboptimal control laws that will<br />
be tested on an other cycle. In this section, the<br />
FTP72 cycle has been chosen, for its realism of<br />
urban driving.<br />
Two <strong>di</strong>fferent control laws will be tested : the first<br />
one, based on Optimization results from Pontryagin<br />
principle, consists of varying the value of p<br />
regar<strong>di</strong>ng to the state of charge, to control u(t),<br />
then the electric motor. The reference Lagrange<br />
multiplier value p is the mean of optimal values of<br />
p, obtained on Artemis Urban cycle with off-line<br />
optimization using PMP algorithm.<br />
The second one uses a map of electric motor<br />
torque created by the optimization results on<br />
Urban Artemis cycle. The electric motor torque<br />
from the map is then weighted by the state of<br />
charge of the battery : reduced if the SOC is<br />
low, increased if the SOC is high. The obtained<br />
results are <strong>di</strong>splayed in Table 1. For the mild hybrid<br />
configuration, the suboptimal laws give fuel<br />
consumptions which are close to the optimal one.<br />
Table 1. Fuel Consumption<br />
Consump. Th. Optimal p-control Elec. mot.<br />
(l/100km) veh. control based torq. map<br />
Mild-H. 3.32 3.22 3.23 3.23<br />
(-3,01%) (-2,71%) (-2,71%)<br />
Mild-H with 2.86 2.87 2.88<br />
Stop’n go. (-13,62%) (-13,49%) (-13,33%)<br />
Full-H. 2.70 2.83 2.86<br />
(-18.67%) (-14,76%) (-13,85%)<br />
For the full hybrid architecture, the two control<br />
laws give degraded results compared to optimal<br />
results. Many reasons can explain these <strong>di</strong>fferences.<br />
First, even if Urban Artemis cycle <strong>and</strong><br />
FTP72 cycle are both realistic of an urban driving,<br />
operating points are very <strong>di</strong>fferent. While<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 76
Request Torque<br />
110<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
Engine operating points<br />
Electric motor operating points<br />
Requested operating points<br />
Optimal operating point lines<br />
1000 1500 2000 2500 3000 3500 4000 4500<br />
Engine Speed<br />
Fig. 4. Operating points of engine in Mild-<strong>Hybrid</strong> mode obtained by PMP algorithm for the urban<br />
Artemis Driving Cycle.<br />
Requested Torque<br />
100<br />
80<br />
60<br />
40<br />
20<br />
0<br />
Optimal operating point line<br />
Engine operating points<br />
Electric motor operating points<br />
Requested operating points<br />
−20<br />
1000 1500 2000 2500 3000 3500 4000 4500<br />
Engine speed<br />
Fig. 5. Operating points of engine in Full-hybrid mode obtained by PMP algorithm for the urban Artemis<br />
Driving Cycle.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 77
equested operating points of Artemis cycle are almost<br />
uniformly located in the whole engine speed<br />
<strong>and</strong> torque space, all requested operating points of<br />
FTP72 are below ω = 3200 rpm, with a majority<br />
below ω = 2000 rpm. The consequence is a unadapted<br />
electric motor map for the second control<br />
law. Concerning the first control law, the optimal<br />
p (obtained with PMP algorithm on FTP72) is<br />
quite <strong>di</strong>fferent from the optimal p obtained for<br />
Artemis cycle, lea<strong>di</strong>ng to degraded results.<br />
Nevertheless, the consumption gain remains high :<br />
−14.76%.<br />
These results illustrate that several driving cycles<br />
are needed to develop efficient suboptimal control<br />
laws based on p-control or electric motor map.<br />
The vehicle speed (related to engine speed by gear<br />
ratios) could also be taken into account to improve<br />
fuel consumption gains.<br />
6. CONCLUSIONS<br />
In this study, we have presented two methods<br />
for optimal control optimization. The heuristic<br />
method based on Pontryagin Minimum Principle,<br />
well known in the free state constraint case, has<br />
been applied successfully to our state constrained<br />
problem, with very similar results to Dynamic<br />
Programming methods <strong>and</strong> a computation time<br />
<strong>di</strong>vided by 100. Nevertheless, there is currently no<br />
theoretical proof to confirm the presented validation<br />
results. Moreover, there are some limitations<br />
to this approach, mainly the assumptions on the<br />
fuel consumption map, modelled by a smooth convex<br />
function of control u (2-order polynomial) ;<br />
this limitation could lead to a bad approximation<br />
of the real fuel consumption for some particular<br />
engines.<br />
Other degrees of freedom, as the gear-shifting<br />
sequence should also be taken into account in<br />
the optimization problem to improve the fuel consumption<br />
gain. Reduction of pollutant emissions<br />
will also be stu<strong>di</strong>ed by considering a second state<br />
based on exhaust temperature.<br />
From optimization results are derived two types of<br />
suboptimal feedback laws based on state of charge<br />
measurements. These laws give encouraging results<br />
even if it needs to be improved in the full<br />
hybrid case.<br />
REFERENCES<br />
André, M. (2004). The artemis european driving<br />
cycles for measuring car pollutant emissions.<br />
Science of The Total Environment 334-<br />
335, 73–84.<br />
Bryson, E. <strong>and</strong> Y.C. Ho (1975). Applied Optimal<br />
Control. Hemisphere Pub. Corp.<br />
Delprat, S. (2002). Evaluation de stratégies de<br />
comm<strong>and</strong>e pour véhicules hybrides parallèles.<br />
PhD thesis. Université de Valenciennes et du<br />
Hainaut-Cambresis.<br />
Evans, Lawrence C. (2000). An Introduction To<br />
Mathematical Optimal Control Theory. University<br />
of California Berkeley.<br />
German, J.M. (2003). <strong>Hybrid</strong> powered vehicles.<br />
Society of Automotive Engineers (SAE).<br />
Guilbaud, T. (2002). Méthodes numériques pour<br />
la comm<strong>and</strong>e optimale. PhD thesis. Université<br />
de Paris VI.<br />
Hartl, Richard F., Suresh P. Sethi <strong>and</strong> Raymond<br />
G. Vickson (1995). A survey of the<br />
maximum principles for optimal control problems<br />
with state constraints. SIAM Review.<br />
Pontryagin, L.S., V.G. Boltyanskii, R.V. Gamkrelidze<br />
<strong>and</strong> E.F. Mishchenko (1974). Théorie<br />
mathématique des processus optimaux. E<strong>di</strong>tions<br />
Mir moscou.<br />
Rousseau, G., D. Sinoquet <strong>and</strong> P. Rouchon (2006).<br />
Constrained optimization of energy management<br />
for a mild-hybrid vehicle. E-COSM -<br />
Rencontres Scientifiques de l’IFP.<br />
Sciarretta, Antonio, Lino Guzzella <strong>and</strong> Michael<br />
Back (2004). A real-time optimal control<br />
strategy for parallel hybrid vehicles with onboard<br />
estimation of the control parameters.<br />
Procee<strong>di</strong>ngs of IFAC Symposium on Advances<br />
in Automotive Control AAC04 pp. 502–507.<br />
Scor<strong>di</strong>a, J. (2004). Approche systématique de<br />
l’optimisation du <strong>di</strong>mensionnement et de<br />
l’élaboration de lois de gestion d’énergie de<br />
véhicules hybrides. PhD thesis. Université<br />
Henri Poincaré - Nancy 1.<br />
Tilagone, R. <strong>and</strong> S. Venturi (2004). Development<br />
of natural gas demonstrator based on an urban<br />
vehicle with a down-sized turbocharged<br />
engine. Oil <strong>and</strong> Gas Science <strong>and</strong> Technology<br />
59(6), 581–591.<br />
Wu, B., C-C. Lin, Z. Filipi, H. Peng <strong>and</strong><br />
D. Assanis (2002). Optimization of power<br />
management strategies for a hydraulic hybrid<br />
me<strong>di</strong>um truck. Procee<strong>di</strong>ng of the 2002<br />
Advanced Vehicle Control Conference, Hiroshima,<br />
Japan.<br />
ACKNOWLEDGMENTS<br />
We would like to thank Gilles Corde, Philippe<br />
Moulin <strong>and</strong> Antonio Sciarretta for helpful <strong>di</strong>scussions<br />
<strong>and</strong> advice at various stages of the elaboration<br />
of this work. We acknowledge Quang Huy<br />
Tran for his advice on numerical methods.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 78
PERFORMANCE TESTING OF HYBRID VEHICLES IN BARI DOWNTOWN<br />
L. Mangialar<strong>di</strong>, L. Soria, N. Caccavo, G. Carbone<br />
Dipartimento <strong>di</strong> Ingegneria Meccanica e Gestionale, Politecnico <strong>di</strong> Bari, Bari (IT)<br />
Abstract: The analysis of homologation rules ECE 91/441 <strong>and</strong> further mo<strong>di</strong>fications has<br />
moved the authors of this paper to investigate how a driving cycle taking place in the<br />
realistic traffic con<strong>di</strong>tions of a town could lead to <strong>di</strong>fferent results in terms of fuel<br />
consumption, when compared to the ones obtained by cars manufacturers in respect of the<br />
st<strong>and</strong>ard cycles proposed by the European St<strong>and</strong>ards. By this, two driving cycles have<br />
been considered <strong>and</strong> experimented in the city of Bari, Italy, one following a urban route,<br />
the other taking place on a suburban track. The experiments have been carried out<br />
utilizing two <strong>di</strong>fferent <strong>Hybrid</strong> Electric <strong>Vehicles</strong> provided by two lea<strong>di</strong>ng <strong>and</strong> competing<br />
car Manufacturers. The analysis of those experiments has shown which architecture can<br />
be more suitable for final users, <strong>and</strong> how far the homologation st<strong>and</strong>ards are from reality.<br />
Also the theoretical amount of kinetic energy that could be recovered thanks to this class<br />
of passenger cars has been investigated.<br />
Keywords: HEV, series/parallel hybrid vehicles, ECE 91/441 cycle, regenerative energy,<br />
fuel consumption.<br />
1. ARCHITECTURE OF HYBRID ELECTRIC<br />
VEHICLES<br />
The in<strong>di</strong>cation “<strong>Hybrid</strong> Vehicle” sometimes is not<br />
enough to precisely identify the architecture of the<br />
vehicle under consideration, as behind the same<br />
name many <strong>di</strong>fferences are hidden especially<br />
depen<strong>di</strong>ng on the ‘mission’ of the vehicle. That is<br />
why it is necessary to analyze this various<br />
typologies.<br />
1.1 HEV Components <strong>and</strong> classification<br />
Before describing the <strong>Hybrid</strong> Electric <strong>Vehicles</strong><br />
(which will be referred to as HEV) classes, it is<br />
necessary to briefly summarize the components that<br />
typically can be found on board of any of these<br />
vehicles.<br />
On all HEV one can always find an internal<br />
combustion engine (ICE), an electric machine (also<br />
called motor), a battery pack, a power converter <strong>and</strong><br />
a transmission, that mechanically links engines to<br />
wheels.<br />
The way by which these components match,<br />
generates a <strong>di</strong>fferent classification of HEV:<br />
- Series <strong>Hybrid</strong>;<br />
- Parallel <strong>Hybrid</strong>;<br />
- Series –Parallel <strong>Hybrid</strong>;<br />
- Complex <strong>Hybrid</strong>.<br />
The complete panorama of HEV classes is showed in<br />
fig.1 (see Cerami, 2005, Genta, 2000).<br />
Fig. 1. Classification scheme of HEVs<br />
To completely develop the potentiality of HEV it is<br />
necessary to design carefully what is called the<br />
Power Management, that is the control strategy<br />
which determines the management <strong>and</strong> use of power<br />
sources. Usually this control strategy is operated by a<br />
control unit which can coor<strong>di</strong>nate the hybrid system<br />
to satisfy certain aims such as fuel saving, polluting<br />
emissions reduction <strong>and</strong> performances optimization<br />
(see Amelia, 2005; Szumanowski, 2000).<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 79
Although the Power Management depends on<br />
the vehicle architecture, we can identify some<br />
common characteristics:<br />
1. the electric machine can work as an<br />
electromechanical converter in order to<br />
assure the power flow from batteries to<br />
wheels <strong>and</strong> vice-versa;<br />
2. batteries can be recharged during<br />
decelerations <strong>and</strong>/or braking (Regenerative<br />
Braking);<br />
3. it is possible to move the vehicle only by<br />
the electric machine, in order to obtain a<br />
complete Zero Emissions Vehicle (but not<br />
for all the hybrid vehicles);<br />
4. in case of vehicle stop or in other<br />
circumstances, when the driver does not<br />
require power, the thermal engine can be<br />
switched off (Idle Stop Mode), with a<br />
consequent fuel saving <strong>and</strong> a temporary<br />
interruption of emissions (see Westbrook,<br />
2001).<br />
1.2 The hybrid vehicles utilized for the tests<br />
The HEVs considered for the investigation have been<br />
two cars competing on the European market: the<br />
Toyota Prius <strong>and</strong> the Honda Civic IMA (Integrated<br />
Motor Assist) (see fig. 2). These two vehicles have a<br />
<strong>di</strong>fferent architecture (Prius is a series/parallel<br />
hybrid, Civic IMA is a parallel one) but they are<br />
comparable in terms of weight (see Toyota Prius,<br />
Caratteristiche Nuovo Modello, 2003, <strong>and</strong> Honda,<br />
Gamma Civic’04, 2003).<br />
Fig. 2. The two utilized cars: Toyota Prius (left) <strong>and</strong><br />
Honda Civic IMA<br />
As a consequence of the <strong>di</strong>fferent architecture the<br />
power management is of course <strong>di</strong>fferent in the two<br />
cases: in the parallel architecture of Honda the motor<br />
only gives an “assist” (overboost effect) when the<br />
driver asks for more torque, whereas in the Toyota<br />
case the motor can work also in synergy with the<br />
combustion engine. In fact on the Toyota <strong>Hybrid</strong><br />
System the motor can, under certain con<strong>di</strong>tions,<br />
move the car on its own, creating in this way, a Zero<br />
Emissions Vehicle (ZEV). Moreover the<br />
transmission of the Honda Civic is a classic<br />
mechanical five gears gearbox, while on the Toyota,<br />
torque is transferred to wheels thanks to an epicyclic<br />
gear which is automatically controlled.<br />
2. TESTS<br />
Before getting in production, each car is subjected to<br />
a series of tests aiming to measuring the fuel<br />
consumption <strong>and</strong> polluting emissions by using<br />
st<strong>and</strong>ard procedures as to make the results<br />
comparable.<br />
2.1 ECE Directives<br />
Measurements take place in closed chambers under<br />
controlled atmosphere, where the vehicle is placed on<br />
a “rolling-test bench” which is able to vary the<br />
resistance force <strong>and</strong> therefore simulate the rolling<br />
resistance of tyres <strong>and</strong> the aerodynamic drag. The<br />
test is carried out by a driver who continuously<br />
follows the velocity cycle <strong>and</strong> the gear shift sequence<br />
(shown on a screen) as requested by the European<br />
St<strong>and</strong>ards. The tests are completed with the analysis<br />
of the exhaust gases operated by an instrumentation<br />
downstream the car exhaust pipe. It is interesting to<br />
point out that among the European Countries it exists<br />
a sort of st<strong>and</strong>ar<strong>di</strong>zation for what concerns the<br />
collection of polluting emissions <strong>and</strong> the analysis of<br />
the fuel consumption data. But, not the same happens<br />
in the case of the sequences of accelerations, speeds<br />
<strong>and</strong> gear shifting that has to be followed during the<br />
tests. Nowadays, several st<strong>and</strong>ard cycles exist (five<br />
are the most important) which reproduce the average<br />
use of passenger cars in Europe, United States <strong>and</strong><br />
Japan. In Europe, at the end of the ‘60s, the<br />
environment <strong>and</strong> energy saving aspects have lead to<br />
the birth of the international commissions, whose<br />
goal was the monitoring of real traffic con<strong>di</strong>tions in<br />
<strong>di</strong>fferent urban textures. These commissions<br />
generated a series of judging criteria which gave life<br />
to the European Directive ECE R15-04 which has<br />
been utilized till to a few years ago. The ECE R15-04<br />
cycle was made of an ideal track of 1013 meters to<br />
be repeated four times at the following con<strong>di</strong>tions: (i)<br />
average speed of 18.7 km/h, (ii) maximum speed of<br />
50 km/h <strong>and</strong> (iii) duration time of engine idling mode<br />
equal to 31% or total running time. Later –in 1993–<br />
in order to take into account also higher vehicle<br />
speeds, the European Ministry Council approved a<br />
new homologation cycle, the ECE 91/441, that<br />
mo<strong>di</strong>fied the previous one by ad<strong>di</strong>ng a new piece of<br />
track at higher speed for a total length of 11 km. The<br />
average <strong>and</strong> maximum speeds in this case became<br />
respectively of 32.5 <strong>and</strong> 120 km/h. At the same time<br />
more severe restrictions were put on polluting<br />
emission limits, this was the Directive Euro 1.<br />
Directives Euro 2, 3 until 4 follow substantially the<br />
same methodology but imposing more <strong>and</strong> more<br />
severe restrictions.<br />
2.2 Merits <strong>and</strong> lacks of the ECE st<strong>and</strong>ards<br />
From the given information it is clear that the<br />
homologation <strong>di</strong>rective 91/441 <strong>and</strong> its further<br />
mo<strong>di</strong>fications offers some important advantages:<br />
• fixing the test parameters, they allow a<br />
<strong>di</strong>rect comparability among the<br />
performances of <strong>di</strong>fferent vehicles operating<br />
in similar con<strong>di</strong>tions;<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 80
• the cycle is of great utility in the statistical<br />
study of vehicles reliability in long periods,<br />
offering con<strong>di</strong>tions that are easily<br />
reproducible in industrial environments.<br />
Unfortunately, to this positive notes some evident<br />
limitations are opposed:<br />
o the cycle does not reproduce the real<br />
driving style of an average driver,<br />
especially in metropolitan areas where the<br />
traffic con<strong>di</strong>tions are more severe <strong>and</strong> the<br />
vehicle is subjected to a higher frequency<br />
of “stop-&-go”;<br />
o the ECE cycle does not follow any realistic<br />
urban topography, it is just an ideal track,<br />
not related at all to the actual traffic<br />
con<strong>di</strong>tions, fuel consumption <strong>and</strong> polluting<br />
emissions which can be encountered in day<br />
life;<br />
o recorded data on fuel consumption result<br />
fake: in particular they show fuel<br />
consumptions to be better than realistic<br />
values, provi<strong>di</strong>ng to the user, in this way,<br />
not completely reliable in<strong>di</strong>cations;<br />
o the measured emissions – <strong>di</strong>rectly<br />
depen<strong>di</strong>ng on the amount of burnt fuel –<br />
may be altered <strong>and</strong>, by consequence,<br />
polluting emission values can be higher<br />
than the ones obtained respecting the<br />
European st<strong>and</strong>ards.<br />
Because of the aforementioned limitations <strong>and</strong> due to<br />
the fact that actual st<strong>and</strong>ards, having been developed<br />
on the basis of stu<strong>di</strong>es of more than forty years ago,<br />
do not provide such realistic consumption values as<br />
to support the final user with reliable information, an<br />
analysis of fuel consumptions in realistic traffic<br />
con<strong>di</strong>tions is needed. The European Community<br />
scientific society does agree with these outlines as<br />
witnessed by the creation of the Artemis cycle – in<br />
many ways similar to the ones realized in this work –<br />
proposed by some research institutes leaded by the<br />
TNO (NL) as a valid alternative to the actual norms<br />
(see TNO Report, 2003).<br />
The traffic con<strong>di</strong>tions under consideration are those<br />
that can be encountered in the city of Bari. The<br />
topography of the city shows an average sidewalk<br />
length shorter than the typical middle European<br />
town (which may be better represented by the ECE<br />
cycle because of their smaller number of stop-&-go),<br />
<strong>and</strong> closer to that of the southern Europe towns.<br />
2.3 Track choice<br />
In order to have a complete scenario of a driver real<br />
ride, the test was split in two tracks:<br />
1. urban cycle<br />
2. suburban cycle.<br />
As a starting point it was chosen the Dipartimento <strong>di</strong><br />
Ingegneria Meccanica e Gestionale (DIMeG),<br />
located in Japigia <strong>di</strong>strict in the southern part of the<br />
city. The Urban cycle (also referred to as the slow<br />
test) has been conceived with speeds always lower<br />
than 50 km/h (law limit). From the DIMeG the two<br />
vehicles moved towards the downtown, where<br />
offices <strong>and</strong> shops are located, drawing a closed ring<br />
track; tests were performed during daytimes, from<br />
8.30 – 9.30 a.m. to 1.00 – 1.30 p.m., when the traffic<br />
con<strong>di</strong>tions are critical. The total length of this track is<br />
of 9 km <strong>and</strong> 300 meters.<br />
The Suburban cycle (the so called fast test) is,<br />
instead, a route passing close to the city centre<br />
(without entering in it), <strong>and</strong> later moving (still 50<br />
km/h speed limit) towards the external ring of the<br />
city. Entering the ring the driver keeps an higher<br />
constant speed (90 km/h) which leads him to leave<br />
the ring at the Bari’s southern extreme exit, thus<br />
entering the Japigia <strong>di</strong>strict. The length of this track<br />
is of 12 km <strong>and</strong> 300 meters.<br />
For each car one slow test <strong>and</strong> one fast were carried<br />
out each day. One day the order was first the slow<br />
test <strong>and</strong> then the fast one, the day after the inverse<br />
order was followed.<br />
The two tests were characterized by the following<br />
data:<br />
Urban test:<br />
• maximum allowed speed: 50 km/h<br />
• pre<strong>di</strong>cted average speed: 18km/h<br />
• pre<strong>di</strong>cted maximum number of stops: 42,<br />
split in:<br />
a. stops <strong>and</strong> priorities: 15<br />
b. traffic lights: 27<br />
• average <strong>di</strong>stance between two stops: 220 m<br />
(approx.)<br />
Suburban test:<br />
• maximum allowed speed:<br />
o 50 km/h inside city walls<br />
o 90 km/h on the ring<br />
• pre<strong>di</strong>cted average speeds:<br />
o 18 km/h inside city walls<br />
o 85 km/h on the ring<br />
o 30 km/h globally<br />
• pre<strong>di</strong>cted maximum number of stops: 24,<br />
split in:<br />
a. stops <strong>and</strong> priorities: 6<br />
b. traffic lights: 18<br />
• average <strong>di</strong>stance between two stops:<br />
a. 512 m (approx.) inclu<strong>di</strong>ng ring<br />
route,<br />
b. 355 m (approx.) exclu<strong>di</strong>ng ring<br />
route (that is 3780 m)<br />
Preventive stop number calculations have been made<br />
considering the worst con<strong>di</strong>tions, so considering a<br />
complete vehicle st<strong>and</strong>still at stops <strong>and</strong> priorities <strong>and</strong><br />
the unfortunate event of always red lamp at traffic<br />
lights.<br />
2.4 Measurement <strong>and</strong> observation modes<br />
Measurements <strong>and</strong> checkouts were of two kinds:<br />
a) “on board”<br />
b) “on ground”.<br />
The “on board” ones consisted of data acquisition<br />
using a laptop linked to a GPS with an external<br />
antenna. This allowed the real time recor<strong>di</strong>ng of the<br />
actual followed routes, thus enabling the calculation<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 81
of the partial <strong>and</strong> total times, effective <strong>di</strong>stances,<br />
instantaneous <strong>and</strong> average speeds, positive <strong>and</strong><br />
negative accelerations, st<strong>and</strong>still <strong>and</strong> constant speed<br />
running times. On the Prius, moreover, there was<br />
also the presence of a real time acquisition system<br />
provided by the Manufacturer itself. This, under<br />
constant control of an on board systems operator,<br />
allowed even to collect running times of each driving<br />
unit (ICE <strong>and</strong> motors), revolution speeds <strong>and</strong> torque<br />
provided by the motors, ICE revolution speeds <strong>and</strong><br />
vehicle speed (this data was later compared with the<br />
one provided by the GPS).<br />
On the Civic IMA the presence of only a<br />
speedometer made more <strong>di</strong>fficult the work of the<br />
operator who had to collect gear shifting <strong>and</strong> stint<br />
times by the use of an electronic chronometer for<br />
every single test. Duty of the driver was, beyond<br />
driving, the in<strong>di</strong>cation of shifting instants <strong>and</strong> gear<br />
ratio used. Gear shifting had to take place by first<br />
bringing the revolution speed of the combustion<br />
engine to the value of 2200 rpm <strong>and</strong> then up-shifting<br />
except for the fifth (last) gear, that was engaged until<br />
the ring’s speed limit is reached.<br />
On ground measurements <strong>and</strong> checkouts were made<br />
in the labs. They consisted of vehicle setups before<br />
tests, <strong>and</strong> ad<strong>di</strong>tional data acquisitions. In detail the<br />
following checkouts were performed:<br />
- fuel tank full;<br />
- accumulators charged;<br />
- on board systems switched on <strong>and</strong> correctly<br />
running;<br />
- air con<strong>di</strong>tioning system switched off;<br />
- car on starting position;<br />
- auxiliary fuel tank weighted;<br />
- refuelling pump weighted;<br />
- (only for Prius) e/v (electric) mode on;<br />
- chronometer present <strong>and</strong> reset;<br />
- laptop charged <strong>and</strong> ready;<br />
- GPS antenna positioned <strong>and</strong> linked;<br />
- (only for Prius) real time acquisition data<br />
system reset <strong>and</strong> connected;<br />
- (for some sample tests) video-camera<br />
positioned <strong>and</strong> ready;<br />
- mileage counter reset;<br />
- tyre pressure checked <strong>and</strong> set.<br />
The fuel tank level check was performed using a<br />
graduated flexible stick. Air con<strong>di</strong>tioning was kept<br />
off in order to avoid the introduction of a <strong>di</strong>sturb<br />
variable in the final consumption data. Starting<br />
position was previously fixed choosing a flat<br />
horizontal zone close to the DIMeG laboratories:<br />
positions of tyres were marked on the ground. Fuel<br />
was refilled using an h<strong>and</strong> pump which allowed an<br />
accurate control of the amount of liquid provided, an<br />
auxiliary tank of 5 liters was used to this end. A<br />
precision balance was used for weight<br />
measurements. The auxiliary tank was weighted<br />
before <strong>and</strong> after each refill together with the h<strong>and</strong><br />
pump in order to take into account any possible<br />
residual quantity of fuel.<br />
Concerning the fuel, it was always bought from the<br />
same company, Total Italia Spa. The same company<br />
provided official documents declaring specific<br />
weight of gasoline <strong>and</strong> its origin. Every day the data<br />
concerning the meteorological con<strong>di</strong>tions were<br />
acquired at the DIMeG (humi<strong>di</strong>ty, temperature,<br />
pressure, etc.). Before every test tyre pressure was<br />
checked <strong>and</strong> possibly set using a <strong>di</strong>gital manometer<br />
<strong>and</strong> an air compressor.<br />
3. RESULTS<br />
A total number of 35 tests were performed using the<br />
two mentioned vehicles; for each test the kinematic<br />
data were collected by the GPS <strong>and</strong> fuel consumption<br />
data –as said before– by the <strong>di</strong>rect measurement.<br />
3.1 Meteorological con<strong>di</strong>tions<br />
After collecting temperature, relative humi<strong>di</strong>ty,<br />
atmospheric pressure <strong>and</strong> precipitation data, an<br />
attempt was made to find a <strong>di</strong>rect link between<br />
weather con<strong>di</strong>tions <strong>and</strong> tests duration, as one can<br />
think that a raining event can push more users to<br />
engage the road net. However, measurements showed<br />
that the duration time increased specially during<br />
intense raining but less or even <strong>di</strong>d not increase<br />
during weak phenomena. In fact, there were days<br />
when in spite of a dry weather, particularly long<br />
duration times were recorded. The comparison<br />
between the weather situation <strong>and</strong> test duration<br />
showed a significant correlation only in suburban<br />
tests case; in urban tests there was no apparent <strong>di</strong>rect<br />
connection. Theoretically this phenomenon can be<br />
explained by observing that the ring traffic is affected<br />
by less variables than the city traffic. The workers or<br />
commuters that have to cover long <strong>and</strong> middle range<br />
<strong>di</strong>stances will indeed use their cars anyway, either in<br />
case of rain or in case of sun; on the contrary, city<br />
centre traffic is subjected to factors that may be not<br />
only related to meteorological phenomena.<br />
3.2 IMA time<br />
Being a parallel hybrid vehicle, the Honda Civic<br />
internal combustion engine is continuously running<br />
during the ride (except during st<strong>and</strong>stills in “idle stop<br />
mode”). In this case the main data, which were<br />
collected, concerned the motor inserting time, i.e. the<br />
periods of time during which the electric machine<br />
was provi<strong>di</strong>ng torque (“Assist mode”). The obtained<br />
values are shown in figure 3.<br />
[% on total time]<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
HONDA CIVIC IMA<br />
IMA Assist Time<br />
URBAN TEST: IMA Time<br />
SUBURBAN TEST: IMA Time<br />
Fig. 3. IMA assist time. Columns show the motor<br />
inserting time in percentage on total tests<br />
duration<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 82
One can note that the driving style significantly<br />
influences the motor insertion: in fact, the motor<br />
gives its contribute depen<strong>di</strong>ng on the torque dem<strong>and</strong><br />
from the driver: the more intense <strong>and</strong> longer the<br />
power dem<strong>and</strong> is, the more the insertion lasts. Since<br />
we adopted a soft driving style, the electric assist was<br />
– in terms of time – rather low.<br />
Driving in suburban cycle, of course, required higher<br />
power because of the higher average velocities. This<br />
of course turned out in longer motor insertion times.<br />
3.3 ICE insertion periods<br />
Prius data more carefully analyzed were concerned<br />
with the internal combustion engine running. The<br />
<strong>di</strong>fferent architecture of the car (series/parallel), in<br />
fact, allowed only a minimum driver’s autonomy in<br />
the choice of which driving unit to use. So, having<br />
given priority to the use of the motor, it came out that<br />
the endothermic engine running time was, in the<br />
series/parallel architecture of the Prius, much less<br />
than in the parallel one of Honda (fig. 4).<br />
Differences in insertion times can be explained<br />
considering that during each experiment, the use of<br />
the electric propulsion was always preferred.<br />
[% on total test time]<br />
100<br />
90<br />
80<br />
70<br />
60<br />
50<br />
40<br />
30<br />
20<br />
10<br />
0<br />
TOYOTA PRIUS<br />
Endothermic Engine Running Times<br />
URBAN TEST: ICE Running Time<br />
SUBURBAN TEST: ICE Running<br />
Time<br />
Fig. 4. ICE running time shown as a percentage on<br />
total test duration<br />
Now, the electric propulsion is subor<strong>di</strong>nated to the<br />
battery state of charge <strong>and</strong> the avoi<strong>di</strong>ng of 50 km/h<br />
spee<strong>di</strong>ng (that is also the road code limit). The<br />
Toyota Power Management, indeed, was such to<br />
insert the ICE when this speed value was exceeded.<br />
In this way the ICE was running only in few<br />
occasions as during the (rare) requests of torque<br />
surplus, <strong>and</strong> when the low batteries state of charge<br />
was reached. This led to a lower time percentage use<br />
of the internal combustion engine with respect to the<br />
total ride time. On the contrary, in the suburban cycle<br />
– on the ring – where higher average <strong>and</strong> maximum<br />
speeds, over 50 km/h are imposed, the fully electric<br />
propulsion mode was <strong>di</strong>sengaged <strong>and</strong> the IC engine<br />
remains substantially always switched on.<br />
3.4 “Stop-&-Go”<br />
As it clearly appears, the coverage time of a test is<br />
heavily influenced by times <strong>and</strong> durations of stops.<br />
Of course one expects that a urban test has a greater<br />
number of stops <strong>and</strong> re-starts (“stop-&-go”) than a<br />
comparable length suburban one.<br />
Tests in Bari confirmed this expectation showing an<br />
high number of stop-&-go especially in the city<br />
centre. The stop-&-go time were carefully analyzed<br />
together with the duration time in which the vehicles<br />
travelled at constant speed. This allowed to carry out<br />
a comparison with the st<strong>and</strong>ard European cycles.<br />
Concerning stops, the average values were:<br />
• 50 stops per each urban test (9310 m),<br />
equivalent to one stop every 185 metres<br />
approx.<br />
• 28 stops per each suburban test (12300 m),<br />
equivalent to one stop every 440 metres<br />
approx. .<br />
Table 1 shows the percentages on total time during<br />
which the vehicles had no acceleration, that is in<br />
cases of st<strong>and</strong>stills or constant speed motion. Data<br />
are put in comparison with the ones from the<br />
European Directive: one can note that only in the<br />
case of vehicle moving at constant speed in suburban<br />
tests, experimental data are relatively close to the<br />
ones of the European st<strong>and</strong>ards. In all the other cases,<br />
the obtained values <strong>di</strong>ffer remarkably from reality,<br />
thus supporting the conclusion that real city traffic<br />
possesses features which deeply <strong>di</strong>ffers from the<br />
model provided by Community <strong>di</strong>rectives.<br />
Table 1. Comparison European Norm/Tests in Bari<br />
Percentages on<br />
total time<br />
NEDC Norm<br />
Urban<br />
Tests<br />
Suburban Tests<br />
St<strong>and</strong>still 33 41 30<br />
Constant<br />
Speed motion<br />
36 26 38<br />
3.5 Speed<br />
The GPS system allowed the monitoring of the<br />
position <strong>and</strong> velocity of vehicles with relatively good<br />
precision.<br />
Table 2 presents the average speeds recorded during<br />
the execution of tests for both the cars.<br />
Figures 5 <strong>and</strong> 6 show for comparison an example of<br />
speed trends for a vehicle in suburban test <strong>and</strong> the<br />
ECE cycle.<br />
Urban Tests<br />
[km/h]<br />
Suburban Tests<br />
[km/h]<br />
3.6 Acceleration<br />
Table 2. Tests average speeds<br />
Toyota Prius Honda Civic IMA<br />
13.15 13.85<br />
29.39 27.57<br />
As previously mentioned, during the execution of the<br />
tests great care was given to avoi<strong>di</strong>ng sudden<br />
accelerations. This was accomplished by using a soft<br />
driving style, in order to save as much fuel as<br />
possible.<br />
It is important to underline that during normal<br />
driving, it is not always possible to adopt such a<br />
similar driving style. Thus, the measured<br />
consumption data should be considered as close to<br />
the best obtainable values, that represent an inferior<br />
limit.<br />
After collecting acceleration data, they were<br />
processed <strong>and</strong> <strong>di</strong>vided in positive <strong>and</strong> negative<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 83
accelerations <strong>and</strong> sub<strong>di</strong>vided in classes of 0.5 m/s 2 .<br />
Positive accelerations were put in comparison with<br />
the New European Driving Cycle (NEDC) norm,<br />
negative ones were used to calculate the theoretical<br />
amount of energy that can be regenerated by the<br />
electric machines.<br />
Speed [km/h]<br />
120<br />
100<br />
80<br />
60<br />
40<br />
20<br />
Central Stint Speeds<br />
0<br />
450 550 650 750 850 950 1050<br />
Time [sec]<br />
Fig. 5. Example of speed trends during a suburban<br />
test<br />
Fig. 6. ECE Cycle: composition of UDC, Urban<br />
Driving Cycle plus EUDC, Extra Urban Driving<br />
Cycle<br />
Concerning the positive accelerations, the<br />
comparison with the European Directive showed a<br />
substantial <strong>di</strong>fference in the <strong>di</strong>stribution of time<br />
percentages: the Norm, in fact, de<strong>di</strong>cates most of the<br />
time to acceleration classes between 0.5 <strong>and</strong> 1.0<br />
m/s 2 , whereas during realistic tests the major amount<br />
of time during which the acceleration was kept into a<br />
certain class fell in the range between 0 <strong>and</strong> 0.5 m/s 2<br />
(see figure 7). Moreover the Norm does not contain<br />
positive accelerations larger than 1.5 m/s 2 , whereas<br />
in realistic situations they do exist accelerations<br />
beyond this limit. Of course the weight of these is<br />
not prevailing (see figures 8 <strong>and</strong> 9), but one has to<br />
remember that for intense accelerations <strong>and</strong> high<br />
RPM number, the endothermic engine goes through<br />
decreasing efficiency con<strong>di</strong>tions, <strong>and</strong>, by<br />
consequence, faces a worsening of fuel consumption.<br />
Time [%]<br />
45<br />
40<br />
35<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
0-0.5<br />
Comparison on positive accelerations<br />
0.5-1<br />
1-1.5<br />
Acceleration classes [m/sec 2 ]<br />
Positive Accelerations<br />
Suburban Test<br />
Positive Accelerations<br />
ECE Directive<br />
1.5-2 2-2.5 2.5-3<br />
Fig. 7. Comparison between positive accelerations<br />
imposed by the ECE <strong>di</strong>rective <strong>and</strong> real values<br />
obtained during the suburban test<br />
time [%]<br />
40<br />
35<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
[d.s. 5.0]<br />
Urban Tests - Positive Accelerations<br />
[d.s. 0.26]<br />
[d.s. 0.17] [d.s. [d.s. 0.11] [d.s. 0.08]<br />
0 - 0.5 0.5 - 1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0<br />
Acceleration Classes [m/s 2 ]<br />
d.s. = st<strong>and</strong>ard deviation<br />
Fig. 8. Amount of percentage time of acceleration<br />
classes for slow test typology<br />
Time [%]<br />
40<br />
35<br />
30<br />
25<br />
20<br />
15<br />
10<br />
5<br />
0<br />
[d.s. 2.2]<br />
Suburban Tests - Positive Accelerations<br />
[d.s. 0.6]<br />
[d.s. 0.3]<br />
[d.s. 0.3]<br />
d.s. = st<strong>and</strong>ard deviation<br />
[d.s. 0.2] [d.s. 0.03]<br />
Acceleration Classes [m/s 2 0 - 0.5 0.5 - 1.0 1.0 - 1.5 1.5 - 2.0 2.0 - 2.5 2.5 - 3.0<br />
]<br />
Fig. 9. Amount of percentage time of acceleration<br />
classes for slow test typology<br />
3.7 Regenerative energy<br />
An HEV is as more useful as its electric mode<br />
autonomy increases (see Advanced <strong>Hybrid</strong> Vehicle<br />
Powertrains 2005, 2005). Unfortunately one simple<br />
charge of the batteries is not able to provide a good<br />
autonomy, that is why modern HEVs use a<br />
regenerative process consisting of a partial recovery<br />
of the vehicle’s kinetic energy during decelerations.<br />
This is achieved thanks to the electric machine that is<br />
able to work both as a motor <strong>and</strong> as a generator.<br />
During braking <strong>and</strong>/or slowing down, the power<br />
management system switches off both the driving<br />
units <strong>and</strong> the let the wheels to drag in rotation the<br />
electric machine making it work as a generator, thus<br />
recharging the accumulators.<br />
This operation cannot take place in every con<strong>di</strong>tions,<br />
as long lasting or too intense decelerations could<br />
create such thermal <strong>and</strong> vibrational stresses (see<br />
Componenti e Sistemi per Veicoli a Trazione<br />
Elettrica, Parte Seconda, 1991) as to damage the<br />
whole system. Moreover, the braking effect of the<br />
generator alone is not enough to stop the vehicle in<br />
emergency con<strong>di</strong>tions.<br />
Data collected were stu<strong>di</strong>ed by <strong>di</strong>vi<strong>di</strong>ng decelerations<br />
in classes of 0.25 m/s 2 , then it was investigated the<br />
amount of energy that could have been regenerated<br />
per unit of mass, in case the whole vehicle’s kinetic<br />
energy contributed to the regeneration <strong>and</strong> in case<br />
where a couple of hypothesized threshold limits were<br />
reducing the kinetic regenerable energy. Of course<br />
deceleration values excessive for the hybrid system<br />
survival were excluded from the calculation: in<br />
particular classes with module more than 2.0 m/s 2<br />
were ignored. Calculations also excluded<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 84
decelerations under a speed threshold of 20 km/h, as<br />
generally the response time <strong>and</strong> the amounts of<br />
recoverable energy until this value is negligible. In<br />
order to take into account energy losses, a reasonable<br />
value of the efficiency of conversion of about 0.85-<br />
0.90 has to be considered: of course this is an<br />
approximate value as neither Toyota or Honda<br />
provided the actual values. Figures 10 <strong>and</strong> 11 report<br />
the theoretical amounts of regenerative energy<br />
ordered by deceleration classes, expressed in J/kg;<br />
please note that in each <strong>di</strong>agram the two vertical<br />
lines identify the threshold limits which guarantee<br />
the aforementioned system integrity. In fact, as the<br />
real physical limits due to the electric machines was<br />
not known, we assumed two <strong>di</strong>fferent thresholds<br />
related to two <strong>di</strong>fferent level of acceptable<br />
deceleration intensities.<br />
[J/Kg]<br />
1000<br />
900<br />
800<br />
700<br />
600<br />
500<br />
400<br />
300<br />
200<br />
100<br />
0<br />
d.s.: 51<br />
Regenerative Energy - Urban Tests<br />
Negative<br />
accelerations up to<br />
-1.0 m/sec 2<br />
d.s.: 80<br />
d.s.: 80<br />
Negative<br />
accelerations up<br />
to -1.50 m/sec 2<br />
d.s.: 53 d.s.: 80<br />
0 ÷ -0.25 -0.25 ÷ -0.50-0.50 ÷ -0.75-0.75 ÷ -1.0 -1.0 ÷ -1.25-1.25 ÷ -1.50-1.50 ÷ -1.75-1.75 ÷ -2.0<br />
Deceleration classes<br />
[m/sec 2 ]<br />
d.s.: 62<br />
Fig. 10. Regenerative energy, slow test<br />
[J/kg]<br />
1000<br />
900<br />
800<br />
700<br />
600<br />
500<br />
400<br />
300<br />
200<br />
100<br />
0<br />
Regenerative Energy - Suburban Tests<br />
d.s.: 35<br />
d.s.: 120<br />
Negative<br />
accelerations up to<br />
-1.0 m/sec 2<br />
d.s.: 80<br />
d.s.: 40 d.s.:<br />
118<br />
Negative<br />
accelerations up<br />
to -1.50 m/sec 2<br />
Negative<br />
accelerations up<br />
to -2.0 m/sec 2<br />
d.s.: 81<br />
d.s.: 50<br />
0 ÷ -0.25 -0.25 ÷ -0.50-0.50 ÷ -0.75-0.75 ÷ -1.0 -1.0 ÷ -1.25-1.25 ÷ -1.50-1.50 ÷ -1.75-1.75 ÷ -2.0<br />
Deceleration classes<br />
[m/sec 2 ]<br />
d.s.: 100<br />
Fig. 11. Regenerative energy, fast test<br />
3.8 Consumption<br />
[L/100km]<br />
10<br />
9<br />
8<br />
7<br />
6<br />
5<br />
4<br />
3<br />
2<br />
1<br />
0<br />
dev.std: 0.92<br />
Measured<br />
Consumption - Urban<br />
Cycle<br />
HONDA CIVIC IMA<br />
Consumption<br />
Urban Cycle<br />
Omologation<br />
Consumption* -<br />
Urban Cycle<br />
Measured<br />
Consumption -<br />
Suburban Cycle<br />
*: Omologation 1999/100/EC<br />
Negative<br />
accelerations up<br />
to -2.0 m/sec 2<br />
d.s.: 70<br />
Combined/Suburban<br />
Cycle<br />
dev.std: 0.62<br />
d.s.: 65<br />
Omologation<br />
Consumption* -<br />
Combined Cycle<br />
Fig. 12. Honda Civic IMA: comparison<br />
measured/declared consumption<br />
[L/100km]<br />
10<br />
9<br />
8<br />
7<br />
6<br />
5<br />
4<br />
3<br />
2<br />
1<br />
0<br />
Measured<br />
Consumption - Urban<br />
Cycle<br />
TOYOTA PRIUS - Consumption<br />
dev.std: 0.89 dev.std: 0.94<br />
Declared**<br />
Consumption - Urban<br />
Cycle<br />
Measured<br />
Consumption -<br />
Suburban Cycle<br />
**: by Directive 80/1268/EEC reprised by Directive<br />
1999/100/EC<br />
Declared**<br />
Consumption -<br />
Suburban Cycle<br />
Fig. 13. Toyota Prius: comparison measure/declared<br />
consumption<br />
The experiments showed that the measured fuel<br />
consumptions of the two vehicles are not the same as<br />
declared by the Manufacturers during the<br />
homologation. This, of course shows that<br />
homologations obtained using the actual st<strong>and</strong>ards<br />
give not realistic values. The following figures 12<br />
<strong>and</strong> 13 show the summary of measured fuel<br />
consumptions for both the two hybrid cars, <strong>and</strong><br />
compare the urban <strong>and</strong> the suburban test data with<br />
the ones declared by the car Manufacturers.<br />
In both cases, one can note the measured data are<br />
always larger than the declared ones as also shown in<br />
table 3.<br />
Table 3. Toyota, Honda: deviation percentages<br />
between declared <strong>and</strong> measured consumption values<br />
Urban<br />
cycle<br />
declared<br />
[l/100km]<br />
Urban<br />
cycle<br />
measured<br />
(average)<br />
[l/100km]<br />
Deviation<br />
%<br />
Comb.ed<br />
cycle<br />
declared<br />
[l/100km]<br />
Suburban<br />
cycle<br />
measured<br />
(average)<br />
[l/100km]<br />
Deviation<br />
%<br />
Toyota Prius<br />
5.0 6.03 +20.6 4.3 5.69 +32.3<br />
Honda Civic IMA<br />
6.0 8.17 +36 4.9 5.69 +42<br />
4. CONCLUSIONS<br />
This work concerned the study <strong>and</strong> experimental<br />
analysis of two consumption cycles, urban <strong>and</strong><br />
suburban, conceived to verify the correspondence of<br />
the ECE 91/441 cycle <strong>and</strong> its further mo<strong>di</strong>fications,<br />
to the real traffic con<strong>di</strong>tions of a vehicle moving in a<br />
metropolitan town as the city of Bari is.<br />
The experimental analysis, moreover, interested two<br />
motorcars belonging to a rapid development <strong>and</strong><br />
<strong>di</strong>ffusion category, the hybrid vehicles, which are<br />
driven by the combination of two engines: one is the<br />
IC engine <strong>and</strong> one an electric machine.<br />
The analysis put in evidence that the vehicle<br />
performances <strong>di</strong>ffer as a consequence of the <strong>di</strong>fferent<br />
architectures adopted on the two cars.<br />
Between the two considered architectures, the Toyota<br />
series/parallel one appears to be the more promising<br />
from the fuel consumption point of view. In the<br />
Honda’s parallel system, instead, the advantages of<br />
the electric motorization are available only when the<br />
driver requires high values of torque <strong>and</strong> power; so<br />
when a soft driving style is used, the electric motor is<br />
often <strong>di</strong>sengaged.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 85
Regeneration represents a further frontier of<br />
development for HEVs: at the state of the art,<br />
regenerative braking, together with other technical<br />
devices, provides an energetic recovery estimated<br />
around 30% on global consumption by the<br />
Manufacturers. Vibrations <strong>and</strong> working temperatures<br />
of electric components limit this chance; so it clearly<br />
appears that this energy increase passes through the<br />
functional streamlining of electric machines <strong>and</strong> their<br />
related components.<br />
Then, the analysis took in consideration the<br />
homologation cycle ECE in its most recent version<br />
Euro 4. In comparison with it we utilized two<br />
realistic cycles in the city of Bari. Results have<br />
evidenced a significant <strong>di</strong>stance between data<br />
obtained by the Manufacturers respecting the<br />
normative, <strong>and</strong> the ones recorded during the<br />
experimentation. In fact, although during the<br />
experimentation the same acceleration classes of the<br />
norm were respected (assumed as a reference), it was<br />
found out how the single weights <strong>di</strong>ffer. In<br />
agreement on this main lines seems to be the whole<br />
European scientific community. Both the hybrid<br />
vehicles showed the vali<strong>di</strong>ty of their projects <strong>and</strong><br />
allowed to underline a deviation in declared<br />
consumption data that in the best event was of the<br />
20% <strong>and</strong> reached a top of more than 40%, showing,<br />
in this way, all the limitations of the actual European<br />
homologation cycle.<br />
Aknowledgments: the Authors would like to<br />
thank Toyota Motor Italia <strong>and</strong> Honda Automobili<br />
Italia for having provided the two motorcars <strong>and</strong><br />
the Automobile Club d’Italia – Bari that<br />
sponsored the survey.<br />
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Honda, Gamma Civic ’04 (2003). Cartella Stampa<br />
Honda, Verona.<br />
Szumanowski Antoni. Fundamentals of <strong>Hybrid</strong><br />
Vehicle Drives (2000). Warsaw-Radom 2000.<br />
Toyota Prius,Caratteristiche Nuovo Modello (2003).<br />
Serie NHW20, Toyota Motor Publication,<br />
NCF256IT.<br />
Westbrook Michael H.. The Electric <strong>and</strong> <strong>Hybrid</strong><br />
Electric Car (2001). SAE International<br />
Publications.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 86
HYBRID VEHICLES WITH ELECTRICAL MULTI ENERGY UNITS<br />
M. Cacciato, A. Consoli, G. Scarcella, A. Testa<br />
Department of Electrical, Electronics <strong>and</strong> System Engineering<br />
Viale Andrea Doria, 6 - 95125<br />
Catania, Italy<br />
Abstract: In order to evaluate electrical <strong>and</strong> hybrid vehicles performance, mathematical<br />
models of SCs, FCs, <strong>and</strong> PV modules have been implemented in Advanced Vehicle<br />
Simulator. A deep analysis about the advantages of integrate st<strong>and</strong>ard batteries with new<br />
storage devices, as super-capacitors, fuel-cells <strong>and</strong> photo-voltaic modules has been done.<br />
For each electrical units described above, an accurate balance has been done. Moreover,<br />
using a multi-criteria approach a cost-benefit analysis has been performed considering in<br />
a period of ten years, in order to evaluate the economical advantages of using the<br />
ad<strong>di</strong>tional units.<br />
Keywords: Super-capacitors, photo-voltaic modules, ADVISOR, cost-benefit analysis.<br />
1. INTRODUCTION<br />
In the last years, the global request of energy has<br />
increased at high rate <strong>and</strong> the forecasts for the next<br />
future guess a faster rate of growing in the energy<br />
dem<strong>and</strong>. As a consequence, many environmental<br />
problems has been experienced related with the high<br />
percentage of Carbon Oxide (CO), Nitrogen Oxides<br />
(NOx), subtle dusts, etc., present in the atmosphere.<br />
Such a problems are more relevant in urban areas<br />
because of high density of population <strong>and</strong>,<br />
consequently, of the use of polluting devices. In<br />
particular, in the last years an enormous increasing of<br />
the pollution has been experienced due to the rising<br />
number of vehicles. On the other h<strong>and</strong>, conventional<br />
energy sources, as petroleum, are expected to be<br />
exhausted in some tens or, at most, few hundreds of<br />
years. Considering such a scenario, it is essential to<br />
develop ‘clear’ <strong>and</strong> highly efficient vehicles, such as<br />
electrical ones, ‘pure’ or ‘hybrid, that allow to reach<br />
high performance, similarly to those of internal<br />
combustion engine, while using clean energies.<br />
In order to increase the performance of electrical <strong>and</strong><br />
hybrid vehicles, enabling technologies are Super-<br />
Capacitors (SCs), Fuel Cells (FCs) <strong>and</strong> Photo Voltaic<br />
(PV) modules, that can be integrated in hybrid <strong>and</strong><br />
electrical vehicles. To evaluate the vehicles<br />
performance, mathematical models of SCs, FCs, <strong>and</strong><br />
PV modules have been implemented in Advanced<br />
Vehicle Simulator (ADVISOR), developed by the<br />
National Renewable Energy Laboratory (NREL) of<br />
the U.S. Department of Energy. The ADVISOR is a<br />
very flexible tool, implemented in Matlab, that<br />
enables fast <strong>and</strong> accurate performance analysis <strong>and</strong><br />
to calculate fuel savings of conventional <strong>and</strong><br />
advanced, light <strong>and</strong> heavy-duty vehicles, as well as<br />
hybrid electric <strong>and</strong> fuel cell vehicles (A. Brooker et<br />
al., 2002). Using such a tool, a deep analysis has<br />
been done for two vehicles, a car <strong>and</strong> a bus.<br />
Moreover, the cost of <strong>di</strong>fferent solutions has been<br />
considered to evaluate their impact on the vehicles<br />
economy.<br />
2. ADVISOR MODELS<br />
In order to investigate the impact on FC vehicles<br />
performance of new electrical units as SCs <strong>and</strong> PV<br />
modules installed on board, the model of two<br />
electrical vehicles, powered by a FC, has been used.<br />
To this aim, new models of SCs <strong>and</strong> PV modules<br />
have been developed in Matlab/Simulink <strong>and</strong><br />
implemented in such a way to be integrated in the<br />
ADVISOR environment. The great flexibility of<br />
such an approach, allows to easily evaluate many<br />
vehicle configurations in <strong>di</strong>fferent situations <strong>and</strong> to<br />
easily compare the results (A. Ema<strong>di</strong> et al., 2004).<br />
2.1 Car model.<br />
As a reference car, an electrical Mercedes-Benz F-<br />
Cell, has been used. Such vehicle is the electrical<br />
powered version of st<strong>and</strong>ard Class A car, equipped<br />
with a fuel cell <strong>and</strong> a small battery pack to support<br />
the fast power transient during the quick<br />
accelerations. The main vehicle specifications are<br />
reported in Tab. 1 (M. C Pera et al., 2002).<br />
Car<br />
Tab. 1: Main parameters of F-Cell car.<br />
Length [m] 3,838<br />
Width [m] 1,764<br />
Height [m] 1,593<br />
Curb weight [kg] 1509<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 87
Fuel Cell<br />
Electrical<br />
Motor<br />
Battery<br />
Transmission<br />
2.2 Bus model.<br />
Technology PEM<br />
Voltage [V] 250-450<br />
Pressure [bar] 350<br />
Power [kW] 72<br />
Weight [kg] 274<br />
Technology<br />
Induction<br />
Machine<br />
Power [kW] 65<br />
Efficiency 0.94<br />
Maximum current [A] 384<br />
Minimum voltage [V] 200<br />
Weight [kg] 86<br />
Technology Ni-Mh<br />
Voltage [V] 150-250<br />
Power [kW] 15-20<br />
# of mudules 25<br />
Module capacity [Ah] 45<br />
Total weight [kg] 156<br />
Number of gears 1<br />
Gear ratio 9.9<br />
Weight [kg] 108<br />
As a reference bus, the electrical Mercedes-Benz<br />
Citaro, has been used. Such vehicle is electrical<br />
powered <strong>and</strong> equipped with a fuel cell. The main<br />
vehicle specifications are reported in Tab. 2.<br />
Bus<br />
Fuel Cell<br />
Motor<br />
Tab. 2: Main parameters of Citaro bus.<br />
Length [m] 11,95<br />
Width [m] 2,55<br />
Height [m] 3,69<br />
Curb weight [kg] 18.000<br />
Max load [kg] 4900<br />
Producer/Mod.<br />
Ballard<br />
Mark902<br />
Technology PEM<br />
Voltage [V] 760<br />
Current [A] 510<br />
Power [kW] 280<br />
Weight [kg] 238<br />
Technology<br />
Induction<br />
Machine<br />
Power [kW] 187<br />
Efficiency 0.95<br />
Maximum current [A] 540<br />
Minimum voltage [V] 400<br />
Weight [kg] 91<br />
Battery Technology Pb<br />
Transmission<br />
2.3 PV roof.<br />
Voltage [V] 700<br />
Power [kW] 80<br />
# of mudules 66<br />
Module capacity [Ah] 40<br />
Total weight [kg] 800<br />
Producer/Mod. ZF / HP502C6<br />
Number of gears 6<br />
Gear ratio<br />
3,43 2,01 1,42<br />
1,0 0,83 0,59<br />
Weight [kg] 305<br />
It is considered the possibility to integrate a PV<br />
generation system in the roofs of the car <strong>and</strong> bus.<br />
For the car, it is considered to built a PV roof<br />
suitably designed using single PV cells, while for the<br />
bus st<strong>and</strong>ard PV modules have been considered. The<br />
PV roofs parameters are reported in Tab.s 3 <strong>and</strong> 4, at<br />
a ra<strong>di</strong>ance of 1000 W/m2 <strong>and</strong> 25 °C.<br />
Tab. 3: Parameters of car PV roof.<br />
Technology Thin film<br />
Voltage @ open circuit [V] 0,68<br />
Current @ short circuit [A] 0,016<br />
Peak power [mW] 8,5<br />
Cell area [mm 2 ] 45<br />
Cell length [mm] 6,5<br />
Cell weight [g] 0,23<br />
Roof fill factor 0,79<br />
# of cells in series for string 265<br />
# of strings in parallel 162<br />
Total weight [kg] 10<br />
Tab. 4: Parameters of bus PV roof.<br />
Technology Single crystalline<br />
Module length [m] 0,66<br />
Module width [m] 1,48<br />
Module area [m 2 ] 0,98<br />
Module weight [kg] 11,9<br />
# of modules for string 8<br />
# of strings 3<br />
Total weight [kg] 286<br />
Total area [m 2 ] 23,52<br />
Voltage @ open circuit [V] 21,3<br />
Current @ short circuit [A] 8,1<br />
Roof fill factor 0,752<br />
Module peak power [W] 130<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 88
2.4 Super capacitors.<br />
Nowadays, SCs are an emerging class of passive<br />
devices, able to store relevant energy quantities while<br />
working at high power levels. The SCs are derived<br />
from st<strong>and</strong>ard electrolytic capacitors largely used in<br />
power electronic applications which are able to<br />
operate at high power, in ad<strong>di</strong>ction, SCs show a very<br />
high capacitance value per volume, up to one<br />
hundred time the electrolytic capacitors (Barker P. ,<br />
2002).<br />
In high efficiency vehicles, the regenerative braking<br />
is highly desirable, but, the batteries can not be<br />
recharged at the power level of braking, that can<br />
reach the nominal power of the electrical machine.<br />
Therefore, two technical solutions are possible, the<br />
former consists in a partial recovery of the available<br />
energy during the braking, because of the limited<br />
power that can recharge the batteries. The latter,<br />
using a energy buffer like SCs, allows the full<br />
recovery of the breaking energy. The last solution,<br />
although energetically efficient, is more expensive<br />
because of the actual high price of SCs <strong>and</strong> the need<br />
of an auxiliary power converter for controlling the<br />
power flowing trough SCs. The characteristics of<br />
SCs, as reported in Tab.5, well match the<br />
requirements of automotive applications. The<br />
benefits obtainable using such components have been<br />
evaluated.<br />
Tab. 5: Parameters of single SC <strong>and</strong> SC bank.<br />
Voltage @ open circuit [V] 2,4<br />
SC weight [g] 15<br />
ESR d [mΩ] 12,6<br />
Energy density [Wh/kg] 6,1<br />
Power density [W/kg] 3500<br />
# of SCs in a bank 196<br />
Nominal bank voltage[V] 450<br />
Bank power [kW] 10<br />
Total weight [kg] 2,86<br />
2.5 Test cycles.<br />
The test cycles used to evaluate the vehicles<br />
performances are obtained as a combination of<br />
st<strong>and</strong>ard cycles <strong>and</strong> stop periods.<br />
The cycle used to test the F-Cell car is constituted by<br />
two ECE speed profiles, a stop period <strong>and</strong> a st<strong>and</strong>ard<br />
EUDC speed profile, as reported in fig. 1.<br />
Fig. 1. Used test cycle used for F-Cell car.<br />
The elevation is introduced as a parameter. The<br />
cycle used to test the Citaro bus is constituted by two<br />
groups of, respectively, seven <strong>and</strong> five ECE speed<br />
profiles, split by a stop period, as reported in fig. 2.<br />
Fig. 2. Used test cycle for Citaro Bus.<br />
3. VEHICLES PERFORMANCE EVALUATION<br />
3.1 F-Cell Car.<br />
Fig. 3. Matlab scheme of the vehicles with FC, batteries, PV <strong>and</strong> SC units.<br />
Considering the test cycle reported in fig. 1, the<br />
following F-Cell car configurations have been<br />
simulated:<br />
• WES without energy storage systems<br />
• CB with batteries<br />
• UC with super capacitor banks only<br />
• CBUC with batteries <strong>and</strong> super capacitor banks<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 89
The first configuration (WES) consists in the F-Cell<br />
car without any batteries, then, the energy recovery<br />
during the braking operation is not allowed. The<br />
second configuration (CB) uses st<strong>and</strong>ard batteries.<br />
The third configuration (UC), only uses a SCs bank,<br />
while the last configuration exploits both storage<br />
systems, opportunely sized.<br />
In Tab. 6, are reported the car weights in<br />
correspondence of each configuration.<br />
Tab. 6: Gross weight of F-Cell car for <strong>di</strong>fferent<br />
configurations [kg].<br />
Car with PV without PV<br />
Config. roof [kg] roof [kg]<br />
WES 1363 1373<br />
CB 1519 1529<br />
UC 1375 1385<br />
CBUC 1462 1472<br />
In Tab. 7, are reported the simulation results for the<br />
car. For taking into consideration the initial State Of<br />
Charge (SOC) of the storage systems, one or two<br />
letters (xx) are used, in<strong>di</strong>cating the SOC of each<br />
storage system as follows:<br />
• S SOC high (≥ 0,8)<br />
• s SOC low (≤ 0,6)<br />
In red are stressed the results of the worse<br />
performance, while in green the best ones. As can be<br />
noted, the PV roof considerably reduces the fuel<br />
consumption, while the car dynamic performance<br />
slightly worsens because of the weight increasing.<br />
3.2 Citaro bus.<br />
Similarly for F-Cell car, some configurations of<br />
Citaro bus have been simulated considering two<br />
battery technologies:<br />
• WES without energy storage systems<br />
• CBPb with lead-acid batteries<br />
• CBNiMh with NiMh batteries<br />
• UC with super capacitor banks only<br />
• CBUCPb with lead-acid batteries <strong>and</strong> SC banks<br />
• CBUCNiMh with NiMh batteries <strong>and</strong> SC banks<br />
In Tab. 8, are reported the bus weights for each<br />
configuration. In Tab. 9, are shown some of the<br />
simulation result obtained for each configurations<br />
<strong>and</strong> <strong>di</strong>fferent SOCs of each storage system.<br />
Tab. 8: Gross weight of Citaro bus for <strong>di</strong>fferent<br />
configurations.<br />
Bus with PV without PV<br />
Config. roof [kg] roof [kg]<br />
WES 18.000 18.286<br />
CBPb 18.800 19.086<br />
CBNiMh 18.277 18.571<br />
UC 18.020 18.306<br />
CBUCPb 18.508 18.794<br />
CBUCNiMh 18.285 18.571<br />
It is noticeable that, using a PV roof, the fuel saving<br />
is higher with respect to the cases of the car because<br />
of the large extent of the bus roof.<br />
4. COST-BENEFIT ANALYSIS<br />
For each electrical units described above, an accurate<br />
balance has been done, taking into account the<br />
energy saved or recovered by the units <strong>and</strong> power<br />
losses due to each unit efficiency <strong>and</strong> the increment<br />
of the vehicle weight. Such energy balance is<br />
evaluated for the F-Cell car supposing a journal trip<br />
of 2, 8 hours per day, correspon<strong>di</strong>ng to a route of 65<br />
km, obtained combining some st<strong>and</strong>ard cycles. For<br />
the Citaro bus, a daily duty of 16 hours,<br />
correspon<strong>di</strong>ng to a route of 250,5 km has been<br />
considered.<br />
Tab. 7: Parameters of single SC <strong>and</strong> SC bank.<br />
Car<br />
Config.<br />
H2<br />
[litres]<br />
without PV roof<br />
Equivalent Acceleration<br />
Fuel [litres] 0-100 km/h<br />
Max<br />
speed<br />
H2<br />
[litres]<br />
with PV roof<br />
Equivalent Acceleration<br />
Fuel [litres] 0-100 km/h<br />
Max<br />
speed<br />
WES 83,9 5,7 17,7 153,8 79,9 5,4 17,7 154,1<br />
CB S 32,1 2,2 15,3 154,2 31,2 2,1 15,4 154,2<br />
CB s 102,9 7,0 19,8 152,5 98,1 6,6 19,8 152,7<br />
UC S 80,2 5,1 14,0 154,0 71,0 4,8 14,1 154,3<br />
UC s 80,0 5,4 17,8 153,7 75,5 5,1 17,9 154,0<br />
CBUC SS 57,9 3,9 14,8 154,7 53,2 3,6 14,9 154,7<br />
CBUC Ss 60,2 4,1 15,7 154,7 55,2 3,7 15,7 154,7<br />
CBUC sS 89,1 6,0 17,2 153.2 83,7 5,7 17,1 153,6<br />
CBUC ss 91,2 6,2 19,0 153,0 86,2 5,8 19,0 153,3<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 90
Bus<br />
Config.<br />
SOC<br />
Batt. SC<br />
Tab. 9: Parameters of single SC <strong>and</strong> SC bank.<br />
Consumption<br />
[l/100 km]<br />
without<br />
PV roof<br />
with PV<br />
roof<br />
Acc. 0-50 km/h<br />
[s]<br />
without<br />
PV roof<br />
with PV<br />
roof<br />
without<br />
PV<br />
roof<br />
Max speed<br />
[km/h]<br />
with PV<br />
roof<br />
WES // // 1172,0 1119,9 14,7 14,7 81,9 81,9<br />
CBPb<br />
CBNiMh<br />
UC<br />
CBUCPb<br />
CBUCNiMh<br />
0,62 // 1158,8 1109,7 15,4 15,4 82,0 82,0<br />
0,75 // 1049,2 999,8 12,3 12,5 81,8 81,8<br />
0,62 // 1136,7 903,6 15,1 14,4 82,0 81,9<br />
0,75 // 1031,0 819,0 12,0 13,1 81,8 81,8<br />
// 0,55 1112,3 861,8 14,7 13,9 81,9 81,9<br />
// 0,75 1107,3 857,3 13,3 12,5 81,9 81,9<br />
0,62 0,55 1128,8 1077,5 15,1 15,1 81,9 82,0<br />
0,62 0,75 1125,2 1073,9 13,4 13,4 82,0 82,0<br />
0,75 0,55 1066,4 1015,4 12,3 12,4 81,8 81,8<br />
0,75 0,75 1063,6 1012,6 12,1 12,3 81,8 81,8<br />
0,62 0,55 1117,7 1065,0 14,9 14,9 81,9 81,9<br />
0,62 0,75 1113,9 1061,1 13,1 13,2 82,0 81,9<br />
0,75 0,55 1050,1 997,8 12,1 12,3 81,8 81,8<br />
0,75 0,75 1046,6 994,3 11,9 12,1 81,8 81,8<br />
Moreover, taking into account the prices of the fuel<br />
<strong>and</strong> units, with a multi-criteria approach a costbenefit<br />
analysis has been performed, to evaluate the<br />
economical advantages of using the ad<strong>di</strong>tional units<br />
in a period of ten years (Chiodo E., 2005). The<br />
adopted criteria are max speed, max acceleration,<br />
units cost, fuel cost.<br />
The algorithm has been implemented in Matlab as a<br />
tool of the ADVISOR. In Tab. 10, are reported the<br />
Car config.<br />
Tab. 11: Results of the MC analysis for the F-Cell car.<br />
Accel. 0-100 km/h<br />
[s]<br />
Max speed<br />
[km/h]<br />
costs used in the cost analysis, the cost of the fuel<br />
cell is considered as desired in the next future.<br />
Electrical units<br />
costs [€]<br />
Tab. 10: Costs of electrical units.<br />
Pb batteries 100 €/kWh<br />
NiMh batteries 300 €/kWh<br />
SC 80 €/kW<br />
PV 5,4 € / Wp<br />
Savings<br />
[€]<br />
Score<br />
WES 17,3 155,0 0,00 0,00 0,0874<br />
WESFV 17,5 155,0 4.284,00 -1.019,00 0,0632<br />
CBNiMh 14,6 154,0 5.883,00 -2.478,00 0,0233<br />
CBNiMiPV 14,8 154,0 7.287,00 -3.392,00 0,0016<br />
UC 15,4 155,0 4.880,00 -1.405,00 0,0501<br />
UCPV 15,6 155,0 6.284,00 -2.319,00 0,0283<br />
CBUCNiMh 14,1 154,8 5.476,00 -1.931,00 0,0353<br />
CBUCNiMhPV 14,3 154,9 6.880,00 -2.775,00 0,0151<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 91
Bus config.<br />
WES<br />
WESPV<br />
CBPb<br />
CBPbPV<br />
CBNiMh<br />
CBNiMhPV<br />
UC<br />
UCPV<br />
CBUCPb<br />
CBUCPbPV<br />
CBUCNiMh<br />
CBUCNiMhPV<br />
Tab. 12: Results of the MC analysis for the Citaro bus.<br />
Accel. 0-100 km/h<br />
[s]<br />
Max speed<br />
[km/h]<br />
As it is reported in Tab.s 11 <strong>and</strong> 12, the cost-benefit<br />
analysis shows that, for fuel cell car there are no<br />
economical advantages in introducing ad<strong>di</strong>tional<br />
power units, while for the bus it is convenient to use<br />
NiMh batteries instead of led-acid ones, SC banks<br />
<strong>and</strong> PV roof.<br />
5. CONCLUSIONS<br />
In the last years, a relevant increasing of the<br />
pollution has been experienced due to the rising<br />
number of vehicles. It is essential to develop ‘clear’<br />
<strong>and</strong> highly efficient vehicles, such as electrical ones,<br />
that, at the same time, show performance close to<br />
that of internal combustion engine. New<br />
technologies as fuel cells, super-capacitors <strong>and</strong><br />
photo-voltaic modules are now available to<br />
increasing the performance of electrical <strong>and</strong> hybrid<br />
vehicles. In this paper, energy <strong>and</strong> economical<br />
evaluations of vehicles performance using those<br />
components have been done. To this purpose,<br />
mathematical models of SCs, FCs, <strong>and</strong> PV modules<br />
have been implemented in Matlab <strong>and</strong> integrated in<br />
the Advanced Vehicle Simulator, obtaining a very<br />
flexible <strong>and</strong> accurate analysis tool. Using such a<br />
Electrical units<br />
costs [€]<br />
Savings<br />
[€]<br />
Score<br />
12,30 81,90 0,00 0,00 0,0586<br />
12,50 81,90 18.500,00 -1.700,00 0,0536<br />
10,90 81,70 21.000,00 -700,00 0,0525<br />
11,10 81,80 39.500,00 -10.800,00 0,0323<br />
10,60 81,70 9.500,00 26.900,00 0,1034<br />
10,80 81,70 28.000,00 23.800,00 0,0959<br />
11,20 81,80 6.400,00 31.050,00 0,1123<br />
11,40 81,90 24.900,00 31.100,00 0,1105<br />
10,70 81,80 15.525,00 18.075,00 0,0869<br />
10,80 81,80 34.025,00 15.325,00 0,0798<br />
10,50 81,80 8.000,00 25.600,00 0,1011<br />
10,70 81,80 26.500,00 33.350,00 0,1132<br />
simulator <strong>di</strong>fferent solutions have been evaluated <strong>and</strong><br />
interesting results have been obtained <strong>and</strong> reported.<br />
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IEEE 2002 Power Engineering Society Summer<br />
Meeting, 21-25 July 2002 pp. 316 – 320.<br />
Chiodo E. (1991). Strumenti <strong>di</strong> supporto alle<br />
decisioni per la tecnologia e l'ambiente: analisi<br />
multicriteriale deterministica applicata al<br />
progetto dei veicoli elettrici, Manutenzione -<br />
Tecnica e Management.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 92
IMPEDANCE MATCHING FOR PV GENERATOR<br />
Angel Cid-Pastor 1,3 , Luis Martínez-Salamero 2 , Corinne Alonso 1 , Guy Schweitz 3 <strong>and</strong> Ramon Leyva 2<br />
1 LAAS-CNRS, Laboratoire d’Analyse et des Architectures des Systèmes, Toulouse, France<br />
2<br />
ETSE Universitat Rovira i Virgili / Dept. Eng. Electrònica, Elèctrica i Automàtica, Tarragona, Spain<br />
3<br />
EDF R&D / LME Department, Moret sur Loing, France<br />
Abstract.- A comparative analysis between a DC power<br />
transformer <strong>and</strong> a DC power gyrator on equal bases of<br />
operation is presented. Both approaches are used to solve<br />
the problem of maximum power transference from a PV<br />
panel to a DC load. An outdoor measurements system has<br />
been implemented <strong>and</strong> comparative experiments have been<br />
carried out during six hours. Results show that both<br />
approaches are practically equivalent in terms of efficiency.<br />
I. INTRODUCTION<br />
Impedance matching in power electronics basically<br />
means solving the problem of maximum power transfer<br />
between a dc generator <strong>and</strong> a dc load. In particular, the<br />
maximum power transfer from a photovoltaic panel to a<br />
dc load is an important technological problem in many<br />
practical cases dealing with the optimization of a PV<br />
conversion chain.<br />
Although there are many works devoted to the problem<br />
of the maximum power point tracking (MPPT) in a PV<br />
array, only few of them deal with the nature of the power<br />
interface while most of them focus on <strong>di</strong>fferent types of<br />
tracking algorithms. The problem of fin<strong>di</strong>ng the most<br />
appropriate power interface is <strong>di</strong>scussed next. The main<br />
antecedents in the study of matching power interfaces can<br />
be found in the works of Singer <strong>and</strong> Braunstein on the<br />
coupling of a PV array <strong>and</strong> a dc load by means of a dc<br />
transformer with variable transformer ratio [1]-[2].<br />
In this paper, we will study the impedance matching for<br />
the maximum power point tracking (MPPT) in<br />
photovoltaic arrays using power gyrators. It will be<br />
demonstrated that both G-gyrators with either controlled<br />
input or output current can be used to solve the MPPT<br />
problem with similar efficiency to that of conventional<br />
solutions based on the DC-transformer approach.<br />
We will first analyze the matching problem using the<br />
notion of a dc transformer <strong>and</strong> subsequently we will<br />
demonstrate that such problem can be solved by using a<br />
power gyrator. We will compare, by means of an outdoor<br />
test [3], the performances of both systems during 6 hours<br />
of measurements.<br />
The outline of the paper is as follows. Impedance<br />
matching by means of DC transformer is presented in<br />
Section II. In Section III, impedance matching by means<br />
of DC power gyrator is analyzed. An outdoor test for<br />
efficiency evaluation of both systems is presented in<br />
Section IV. A conclu<strong>di</strong>ng <strong>di</strong>scussion is given in Section V.<br />
II. IMPEDANCE MATCHING BY MEANS OF A DC POWER<br />
TRANSFORMER<br />
A. Static operating point of the PV array<br />
A DC-to-DC switching converter can be modeled<br />
accor<strong>di</strong>ng to Middlebrook’s para<strong>di</strong>gm as an ideal DC<br />
transformer whose the transformer ratio n(D) is a function<br />
of the duty cycle. The connection of the PV generator <strong>and</strong><br />
the load using a switching converter as interface is shown<br />
in Fig.1 where both generator <strong>and</strong> load have been modeled<br />
by a first quadrant v-i characteristic.<br />
v<br />
PV<br />
i<br />
+<br />
V1<br />
-<br />
I1<br />
VOLTAGE-TO-VOLTAGE<br />
DC-TO-DC<br />
SWITCHING<br />
CONVERTER<br />
I2<br />
+<br />
V2<br />
-<br />
v<br />
VB<br />
+<br />
-<br />
fo(i)<br />
RL<br />
LOAD<br />
Fig. 1. Matching a PV generator to a DC load using a voltage-tovoltage<br />
DC-to-DC switching converter<br />
The behavior of the converter in steady-state can be<br />
described by means of the following equations<br />
V2<br />
= n(<br />
D)<br />
V1<br />
1<br />
(1)<br />
I 2 = I1<br />
n(<br />
D)<br />
which define a DC ideal transformer.<br />
The DC load can be modeled by means of the following<br />
function v = f( i )<br />
v o B L<br />
= f ( i)<br />
= V + R i (2)<br />
with VB > 0 <strong>and</strong> RL > 0.<br />
which corresponds to the Thevenin equivalent of the<br />
usual DC loads supplied by a PV generator. Namely,<br />
storage batteries, permanent magnet DC motor, shunt DC<br />
motor, electrolysis pool, etc.<br />
From (1) <strong>and</strong> (2) the following function v1 = fin(i1) is<br />
derived<br />
v<br />
1<br />
=<br />
f<br />
in<br />
V2<br />
VB<br />
RL<br />
I 2 VB<br />
RL<br />
( i1)<br />
= = + = + I<br />
2 1<br />
n(<br />
D)<br />
n(<br />
D)<br />
n(<br />
D)<br />
n(<br />
D)<br />
n ( D)<br />
(3)<br />
If we consider that the load is a battery with a very<br />
small equivalent series resistance (RL→0), expression (3)<br />
becomes<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 93<br />
i
VB<br />
v1<br />
= fin<br />
( i1)<br />
≈<br />
(4)<br />
n(<br />
D)<br />
Fig 2 shows the intersection of characteristics fo <strong>and</strong> fin<br />
with the PV curve under <strong>di</strong>fferent hypotheses. In this case,<br />
the <strong>di</strong>rect connection of the load to the panel would<br />
correspond to an operating point (VB) where the output<br />
current of the PV generator is zero. As a matter of fact, the<br />
value of the voltage battery is greater than the open circuit<br />
voltage of the PV generator. It can be deduced from (4)<br />
that the characteristics fin will be placed below fo if n(D) ><br />
1. Therefore, from (4) the intersection point A could be<br />
placed at left side of M for a certain value of duty cycle<br />
D1. On the other h<strong>and</strong>, the intersection point B<br />
corresponds to a duty cycle D2 > D1 since n(D) is an<br />
increasing monotonous function of the duty cycle D [4].<br />
The objective of the converter is to achieve a finop<br />
characteristic so that it intersects with PV curve at the<br />
optimal operating point M.<br />
v<br />
VB<br />
VOC<br />
Fig. 2. PV Array operating points ( n(D) >1, D2 > D1)<br />
v<br />
VOC<br />
Fig. 3. PV array operating points ( n(D) < 1, D2 < D1)<br />
A<br />
C<br />
M<br />
M<br />
B<br />
B<br />
ISC<br />
A<br />
ISC<br />
fo<br />
fin(D1)<br />
finopt<br />
fin(D2)<br />
fo<br />
i<br />
i<br />
fin(D2)<br />
finopt<br />
fin(D1)<br />
Similarly, figure 3 illustrates the case of an operating<br />
point correspon<strong>di</strong>ng to a <strong>di</strong>rect connection (point A) which<br />
is located at the right of M. In this case, it is m<strong>and</strong>atory to<br />
perform the matching with a n(D) < 1. Note that it can be<br />
deduced from (4) that the characteristics fin will be placed<br />
above fo if n(D) < 1. Therefore, from (4) the intersection<br />
point C could be placed at left side of M for a certain<br />
value of duty cycle D2. On the other h<strong>and</strong>, the intersection<br />
point B corresponds to a duty cycle D2 < D1.<br />
The election of converter structure will imply a<br />
restriction in the values of n(D). Therefore, we obtain<br />
values of n(D) < 1 with a buck converter, values of n(D)<br />
> 1 with the boost converter <strong>and</strong> both of them with the<br />
Cuk converter. However, the Cuk converter imposes a<br />
sign inversion at the output port.<br />
B. Operating point trajectory of the PV array<br />
Now, we will analyze the influence of the duty cycle<br />
variations in equation (4) in order to study the trajectories<br />
that allow the <strong>di</strong>splacement of the operating point along<br />
the v-i characteristic curve of the PV array.<br />
Therefore<br />
d(<br />
n(<br />
D))<br />
since > 0<br />
dD<br />
assume n(D) > 0.<br />
dV1 VB<br />
dn(<br />
D)<br />
= −<br />
< 0 (5)<br />
dD 2<br />
n ( D)<br />
dD<br />
On the other h<strong>and</strong>, we can write<br />
in any converter [4] <strong>and</strong> we<br />
dV1<br />
∆V1<br />
= ∆D<br />
(6)<br />
dD<br />
Therefore, we can conclude that increasing the duty<br />
cycle will produce a trajectory to the right along the v-i<br />
curve ( ∆ V1 negative), while decreasing D will result in a<br />
trajectory to the left along the v-i curve irrespective of the<br />
step-up or step-down nature of the converter.<br />
C. Experimental Verification<br />
It has been recently demonstrated that an extremum<br />
seeking algorithm was stable in the sense of Lyapunov<br />
<strong>and</strong> that it could applied to the maximum power point<br />
tracking of a PV generator by using a voltage to voltage<br />
dc-to-dc switching converter in PWM operation [5]. The<br />
circuit performing the MPPT control is illustrated in Fig.<br />
4.<br />
iSA<br />
vSA<br />
Analog<br />
Multiplier<br />
Differentiator<br />
Hysteretic<br />
comparator<br />
Fig. 4. Realization of the MPPT controller<br />
Flip-flop +<br />
Inhibition<br />
delay<br />
Integrator<br />
The PV panel is a solar array of monocrystalline cells<br />
with an open circuit voltage of 22.1 V <strong>and</strong> a nominal<br />
voltage value at the maximum power point of 18 V. Since<br />
the load is a 24 V acid-lead battery, the dc-to-dc<br />
conversion structure must be performed by a boost<br />
structure. Fig.5 shows the practical implementation of a<br />
boost dc-to-dc voltage transformer-based with MPPT<br />
function. The boost parameters are given by L1 = 75 µH,<br />
C1= 12 µF, C2 = 20 µF, V2= 24 V <strong>and</strong> a constant<br />
switching frequency of 150 kHz.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 94<br />
vC
Fig. 5. Practical implementation of a boost converter performing the<br />
MPPT of a PV array<br />
Next, it will be shown the experimental behavior of the<br />
Is, Vs, Ps of the PV generator <strong>and</strong> also the duty cycle of the<br />
boost converter with the extremum-seeking control<br />
algorithm under <strong>di</strong>fferent operating con<strong>di</strong>tions. Fig. 6.a<br />
shows the PV system response after the connection of an<br />
ad<strong>di</strong>tional panel in parallel with the PV generator. As it<br />
can be expected, the current increases while the voltage<br />
remains practically unchanged except in the transient-state<br />
connection. Since the voltage operating point has not<br />
changed, the maximum power point is almost<br />
instantaneously reached. A similar situation is observed in<br />
Fig. 6.b in which the panel previously added is removed.<br />
a)<br />
b)<br />
Fig. 6. Response to a parallel connection <strong>and</strong> removal of an ad<strong>di</strong>tional<br />
panel (Time scale: 10 ms/<strong>di</strong>v).<br />
vC<br />
PS<br />
VS<br />
IS<br />
vC<br />
VS<br />
PS<br />
IS<br />
III. IMPEDANCE MATCHING BY MEANS OF A DC POWER<br />
GYRATOR<br />
A. Static operating point of the PV array<br />
If the voltage to voltage dc-to-dc switching converter of<br />
Fig. 1 is substituted by a voltage to current dc-to-dc<br />
switching converter, i.e., a G-power gyrator [6], the<br />
steady-state equations at both input <strong>and</strong> output ports of the<br />
converter will be given by<br />
I<br />
I<br />
1<br />
2<br />
= gV<br />
= gV<br />
2<br />
1<br />
where g is the gyrator conductance.<br />
From (2) <strong>and</strong> (7), we conclude that the input<br />
characteristics iin = fin (v1) will be expressed as<br />
(7)<br />
2<br />
( VB<br />
+ RL<br />
I 2 ) = gVB<br />
+ g R 1<br />
I1 = fin<br />
( V1)<br />
= gV2<br />
= g<br />
LV<br />
Considering that the load is a battery with an equivalent<br />
series resistance RL→0 the expression (8) becomes<br />
I = f ≈ gV<br />
(8)<br />
1 i n B<br />
(9)<br />
Expression (9) shows that the input current will be<br />
proportional to the battery voltage with a proportionality<br />
factor g (the gyrator conductance).<br />
Figs. 7 <strong>and</strong> 8 show the intersection of characteristics fo<br />
<strong>and</strong> fin with the PV curve in similar situations as those<br />
illustrated in figs. 2 <strong>and</strong> 3 respectively. Fig. 7 describes<br />
the <strong>di</strong>rect connection of the load <strong>and</strong> the PV array<br />
resulting in an operating point located at the left of the<br />
maximum power point. It can be derived from (9) that the<br />
intersection point B can be placed at the right side of M by<br />
an appropriate choice of conductance G (a value of the<br />
gyrator conductance g). If we assume that the intersection<br />
at point B corresponds to a certain value G1 of the gyrator<br />
conductance, then intersection at A will correspond to a<br />
value G2 < G1 as derived from (9).<br />
v<br />
VB<br />
VOC<br />
fin(G2) finopt fin(G1)<br />
A<br />
Fig. 7. PV array operating points. Impedance matching by means of a<br />
G-gyrator (fo(i2) intersects at the left side of M)<br />
Fig. 8, in turn, illustrates the case of a <strong>di</strong>rect connection<br />
at point C, which is located at the right side of point M. By<br />
an appropriate selection of the gyrator conductance (G =<br />
G2) the operating point can be placed at the left side of M<br />
(point A). Increasing the conductance value to G1 (G1 ><br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 95<br />
M<br />
B<br />
ISC<br />
fo<br />
i
G2) will establish the operating point at point B, which is<br />
located at the right side of M.<br />
v<br />
VB<br />
VOC<br />
fin(G2) finopt fin(G1)<br />
A<br />
Fig. 8. PV array operating points. Impedance matching by means of a<br />
G-gyrator. (fo(i2) intersects at the right side of M)<br />
B. Operating point trajectory of the PV array<br />
Now, we will study the influence of conductance g<br />
variations in equation (9) in order to study the trajectories<br />
or the operating point along the v-i curve. Hence,<br />
dI1<br />
dg<br />
= VB<br />
M<br />
> 0<br />
B<br />
C<br />
ISC<br />
fo<br />
i<br />
(10)<br />
Therefore, we can conclude that increasing the gyrator<br />
conductance will result in a trajectory towards the right<br />
( ∆ I1 positive), while decreasing g will result in a<br />
trajectory to the left along the v-i curve.<br />
C. Experimental Verification<br />
In [6, 7, 8], <strong>di</strong>fferent types of power gyrators have been<br />
synthesized <strong>and</strong> classified. Fig. 9 shows the block <strong>di</strong>agram<br />
of a power gyrator of type G with MPPT function. In<br />
order to compare in the same con<strong>di</strong>tions the DC power<br />
transformer of section II with the DC power gyrator we<br />
have selected the same converter structure to implement a<br />
power gyrator, i.e., the boost converter. The boost<br />
converter has a pulsating output current, therefore<br />
accor<strong>di</strong>ng to the definition of power gyrator gave in [7],<br />
the use of a boost converter leads to a power semigyrator<br />
implementation.<br />
PV<br />
Array<br />
Module<br />
iSA = i1<br />
+<br />
vSA = v1<br />
iSA<br />
-<br />
vSA<br />
G<br />
GYRATOR<br />
I1 = gV2 I2 = gV1<br />
g<br />
MPPT<br />
Control<br />
i2<br />
+<br />
v2<br />
-<br />
Battery<br />
24 V<br />
Fig. 9. Block <strong>di</strong>agram of a MPPT of a PV array based on a power<br />
gyrator of type G.<br />
In this case, we would synthesize a G-gyrator inten<strong>di</strong>ng<br />
to transform a voltage source at the output port into a<br />
current source at the input port. Since the regulator<br />
establishes the gyrator characteristics through the control<br />
of current i1, we will call this class of circuits G-gyrators<br />
with controlled input current [6]. Hence, we impose a<br />
sli<strong>di</strong>ng mode surface S(x) = gV2 - i1, where V2 is a constant<br />
voltage.<br />
The analysis of the sli<strong>di</strong>ng-mode induced by<br />
considering S(x) = gV2 - i1 results in a stable equilibrium<br />
point for the boost converter, the characteristic equation<br />
being of zero order.<br />
The practical implementation of a boost-converterbased<br />
G-semigyrator with controlled input current is<br />
shown in Fig. 10 for the set of parameters L1 = 75 µH,<br />
C1= 12µF, C2 = 20 µF <strong>and</strong> V2 = 24 V.<br />
Fig. 10. Practical implementation of a boost-converter-based Gsemigyrator<br />
operating at variable switching frequency with MPPT<br />
function<br />
Note that variable vC depicted in Fig. 4 becomes the<br />
gyrator conductance of the power gyrator (Fig. 10). The<br />
variation of is with constant time-derivative is achieved by<br />
imposing such behavior to the gyrator conductance G.<br />
Next, It will be shown the experimental behavior of Is,<br />
Vs, Ps of the PV generator <strong>and</strong> also de conductance g of<br />
the power semigyrator with the extremum-seeking control<br />
algorithm under <strong>di</strong>fferent operating con<strong>di</strong>tions. Fig. 11.a<br />
shows the PV system response after the connection of an<br />
ad<strong>di</strong>tional panel in parallel with the PV generator. As it<br />
can be expected, the current increases while the voltage<br />
remains practically unchanged except in the transient-state<br />
connection. Since the voltage operating point has not<br />
changed, the maximum power point is almost<br />
instantaneously reached. However, when a <strong>di</strong>fferent<br />
situation is observed in Fig. 11.b in which the panel<br />
previously added is removed. Now, the imposed input<br />
current is too large <strong>and</strong> the operating point of the PV<br />
generator remains during 20 ms in the short-circuit point<br />
delivering zero output power. The PV generator starts to<br />
deliver power when the conductance of the gyrator (g)<br />
<strong>di</strong>minishes until a value that implies a current i1 inside of<br />
v-i characteristic.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 96
a)<br />
b)<br />
g<br />
VS<br />
PS<br />
IS<br />
Fig. 11. Response to a parallel connection <strong>and</strong> removal of an ad<strong>di</strong>tional<br />
panel (Time scale: 10 ms/<strong>di</strong>v).<br />
IV. EFFICIENCY EVALUATION<br />
The overall system efficiency of PV conversion<br />
structure (ηTOTAL) is given by [9]<br />
η = η η η<br />
(11)<br />
TOTAL PV MPPT CONV<br />
where ηPV is the ratio of the maximum available<br />
electrical power of the panel for the entering solar<br />
irra<strong>di</strong>ance, ηMPPT is the ratio of the real available electrical<br />
power of the panel for its maximum available electrical<br />
power <strong>and</strong> ηCONV is the ratio of the power at the<br />
con<strong>di</strong>tioner output for the power at the con<strong>di</strong>tioner input.<br />
Our automatic measuring system provides the values of<br />
ηMPPT <strong>and</strong> ηCONV along a complete day. Figs 12 <strong>and</strong> 13<br />
shows this efficiencies values during an outdoor test of 6<br />
hours. In this test we can compare the efficiencies<br />
performances obtained by means of a DC-power<br />
Transformer MPPT (Fig. 12) <strong>and</strong> by means of a DCpower<br />
gyrator (Fig. 13). The converter efficiency is better<br />
for the case of DC transformer; <strong>and</strong> this could be in part<br />
due to a higher consumption of the control circuitry <strong>and</strong><br />
also to the variable switching frequency of the power<br />
gyrator. In fact, a variation in the switching frequency<br />
could imply a reduction of the converter efficiency. On<br />
the other h<strong>and</strong> the MPPT efficiency is bigger for the DC<br />
power gyrator for low levels of input power.<br />
g<br />
VS<br />
PS<br />
IS<br />
Fig. 12. Measured efficiencies of the boost converter-based voltage<br />
transformer with MPPT function<br />
Fig. 13. Measured efficiencies of the boost converter-based Gsemigyrator<br />
with MPPT function<br />
Table I shows the energy balance <strong>and</strong> the averaged<br />
efficiencies during the 6 hours test. The total efficiency<br />
η T = η MPPT η CONV shows that we obtain slightly better<br />
efficiencies with the matching circuit performed by the<br />
DC transformer.<br />
TABLE I. ENERGY BALANCE AND AVERAGED EFFICIENCIES<br />
Available<br />
Energy<br />
Absorbed<br />
Energy<br />
Output<br />
Energy<br />
η MPPT<br />
ηCONV η T<br />
Transfor<br />
mer<br />
90.2 Wh 88.1 Wh 81.3 Wh 97.7 % 92.2 % 90.1 %<br />
Gyrator 88 Wh 86.5 Wh 77.6 Wh 98.3 % 89.7 % 88.2 %<br />
V. CONCLUSIONS<br />
In this work, we have compared the realization of<br />
impedance matching circuits to track the maximum power<br />
point of a PV array by means of two concepts: the DC<br />
power transformer <strong>and</strong> the DC power gyrator.<br />
A DC transformer-based boost converter has been<br />
implemented to match a lead-acid battery of 24 V with a<br />
PV array.<br />
Workshop "<strong>Hybrid</strong> <strong>and</strong> <strong>Solar</strong> <strong>Vehicles</strong>", November 5-6, 2006, University of <strong>Salerno</strong>, Italy 97
Also, it has been shown that power gyrators of type G<br />
with controlled input current can be used as impedance<br />
matching circuits to track the maximum power point of a<br />
PV array. The selected gyrator structure is the boostconverter-based<br />
G-semigyrator with controlled input<br />
current.<br />
We have compared the dynamic <strong>and</strong> static<br />
performances of both possibilities by means of<br />
experimental verification. An outdoor test has been made<br />
to compare the averaged efficiencies in real con<strong>di</strong>tions.<br />
The dc transformer-based boost converter has only an<br />
averaged efficiency 3 % bigger that the DC gyrator-based<br />
boost converter operating in sli<strong>di</strong>ng mode. It has to be<br />
pointed out that the transformer structure has a better<br />
dynamic performance when larges changes in the<br />
irra<strong>di</strong>ation appear. This is due to the fact that when “the<br />
load is a battery” the input current varies almost<br />
instantaneously for the transformer case, while it takes<br />
some ad<strong>di</strong>tional time in the case of the gyrator because the<br />
changes in the input current follows the change of the<br />
output voltage.<br />
Similar stu<strong>di</strong>es are in progress for other converter<br />
structures like buck converter <strong>and</strong> Cuk converter.<br />
VI. REFERENCES<br />
[1] S. Singer <strong>and</strong> A. Braunstein, “A General Model of Maximum<br />
Power Point Trackers” Procee<strong>di</strong>ngs of MELECON’85. pp 147-151<br />
[2] S. Singer <strong>and</strong> A. Braunstein, “Maximum power transfer from a<br />
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