Effect of Pavement Conditions on Rolling Resistance and Fuel ...

<strong>Pavement</strong> Life Cycle Assessment Workshop
University <strong>of</strong> California, Davis
Davis, California
May 5-7, 2010
<strong>Effect</strong> <strong>of</strong> <strong>Pavement</strong> <strong>Conditions</strong> on Rolling
Resistance and Fuel Consumption
Karim Chatti, Ph.D.
Department <strong>of</strong> Civil & Environmental Engineering
Michigan State University
East Lansing, MI 48824

What do we mean by driving
resistance and rolling resistance?
• air resistance
• rolling resistance
• inertial resistance
• gradient resistance
• side force resistance
• transmission losses
• losses from the use <strong>of</strong> auxiliaries
• engine friction
2

Factors affecting rolling resistance
• Most important factors in rolling resistance:
– Vehicle weight
– Tire inflation
• Less important:
– Vehicle speed
• Least important:
– Tire tread design, composition and width
– Tire temperature
– Road structure and conditions
3

Influence <strong>of</strong> IRI and MPD on RR
(Sandberg, 1997)
• Results <strong>of</strong> coast-down measurements on 34 test sections
• Increases in car RR based on ECRPD results
– at speed <strong>of</strong> 54 km/h:
• IRI from 1 to 10 m/km: increase in RR by 19 %
• MPD from 0.3 to 3 mm: increase in RR by 46 %
– at speed <strong>of</strong> 90 km/h:
• IRI from 1 to 10 m/km: increase in RR by 48 %
• MPD from 0.3 to 3 mm: increase in RR by 72 %
4

<strong>Effect</strong> <strong>of</strong> IRI and MPD on fuel
consumption (TRB special report 286)
5
2 m/km reduction in
roughness (IRI)
10 % reduction in average
rolling resistance
1 to 2% reduction in fuel
consumption
5

Gaps in knowledge
• The understanding <strong>of</strong> the relationship
between pavement surface characteristics
and vehicle fuel consumption is still in
development.
• Current models require improvement.
6

NCHRP 1-45 : <strong>Effect</strong> <strong>of</strong> pavement
conditions on fuel consumption
• Recommend models for estimating the effects <strong>of</strong>
pavement surface condition on VOC. These
models should be able to:
a) Take into account pavement, traffic and
environmental conditions encountered in the US
b) Address the full range <strong>of</strong> vehicle types
7

United States VOC Models
Development
8
Winfrey,
Claffey
1968-1971
Intermediate
Brazil Study
1975-1980
US Data on
1970's Vehicle
France Price
Indexing
1976
Red Book
AASHTO
1978
TRDF VOC Model
1982
MicroBENCOST
VOC
1991-1992
Canada: HUBAM
Alberta
United States:
HIAP
HPMS
HERS
State DOT
FHWA
State DOT
Counties
Municipalities
8

9
World Bank VOC Models
Development
Kenya, India &
Caribbean
1971-1986
TRRL & CRRI
Most recent
model
De Weille
1966
Highway Cost Model
1971
MIT, TRRL & LCPC
HDM-III
VOC
1987
HDM-IV
VOC
1994-2000
Brazil Study
1975-1984
TRDF & TRRL
COBA
LEGEND
Background Work
Major VOC Model
Other VOC Model
Zaniewski et al.
TRDF VOC
1980-82
VETO
NITRR
NZVOC
PMIS
CB-Roads
Source: HDM IV manual
9

HDM 4 Model
10
IFC
f Ptr ,
Paccs
Peng
P tr
P accs
P eng
= Power required to overcome traction forces (kW)
= Power required for engine accessories (e.g. fan belt,
alternator etc.) (kW)
= Power required to overcome internal engine friction (kW)
10

P
tr
F
a
F
g
F
c
1000
2
HDM 4 model (cont.)
F
r
F
i
Tractive power
11
Fa
0.5*
* CDmult * CD * AF *
Aerodynamic forces
Fg M * GR*
g
2
M *
M * g * e
R
Fc max
0,
Nw*
Cs
2
*10
2
Fr CR2*
FCLIM * b11*
Nw CR1*
b12 * M b13*
CR 2 Kcr 2 a0
a1*
Tdsp a2*
IRI a3*
a2
Fi M * a0
a1*arctan
*
a
3
3
DEF
Gradient forces
Curvature forces
Rolling resistance
Surface factor
Inertial forces
11

Field tests matrix
12
Section
ID
<strong>Pavement</strong> Type
AC
PCC
IRI range
(m/Km)
Length
(Km)
Speed
limit
(Km/h)
Test Speed
(Km/h)
Replicates
AB X 1.3 - 8.5 1.44 72 56 72 2
BC X 1.7 - 7 1.6 72 56 72 2
DE X 3.5 - 6 0.48 72 56 72 2
EF X 3.3 - 6 0.64 72 56 72 2
GH X 1.1 - 2.5 4.8 112 88 104 2
JI X 1.5 - 2.6 6.4 80 56 72 2
IJ1 X 1.5 - 2.6 0.64 80 72 88 2
IJ2
X
1.6 80 56 72 2
IJ3 X 0.8 - 4.6 0.48 80 56 72 2
IJ4 X 1.28 72 56 72 2
12

13
Data acquisition system
• The data acquisition system could access and log
data from the vehicle’s Engine Control Unit
(ECU) via On Board Diagnostic (OBD) connector
13

14
Pr<strong>of</strong>ile and Texture Measurements:
MDOT test vehicles
Rapid Travel Pr<strong>of</strong>ilometer
This vehicle measures the ride quality or
smoothness <strong>of</strong> pavements. Operating at
highway speeds, it uses a laser to measure the
pr<strong>of</strong>ile <strong>of</strong> the roadway and an accelerometer to
determine the movement <strong>of</strong> the truck.
Road Surface Analyzer
This equipment computes a Mean Pr<strong>of</strong>ile
Depth (MPD) based on the ASTM Standard
E1845
14

15
Slope surveys: High Precision GPS
• The sampling rate is every 1 second
at highway speed (every 100ft).
• The average error is 0.5 inch per
0.3 miles,
15

16
16

17
Loading conditions
Light truck
Heavy truck
6,210 lb 47,000 lb
17

18
Calibration <strong>of</strong> the HDM 4 fuel
consumption model
Engine and accessories power
Pengaccs
Peng
Paccs
KPea * P max*
Paccs
_ a1
( Paccs
_ a0
Paccs
_ a1
RPM RMPIdle
*
RPM 100 RPMIdle
Rolling resistance Surface factor
CR 2 Kcr 2 a0
a1*
Tdsp a2*
IRI a3*
DEF
18

Engine speed (rpm)
<strong>Effect</strong> <strong>of</strong> engine speed prediction errors
on the calibration
2500
19
Overestimation <strong>of</strong> the engine
speed
2000
1500
Overestimation <strong>of</strong> the engine
and accessories power
1000
500
0
0 20 40 60 80
Speed (Km/h)
measured engine speed
engine speed model (HDM 4)
Underestimation <strong>of</strong> the
traction power
=
Underestimation <strong>of</strong> the effect
<strong>of</strong> pavement conditions
19

Engine speed (rpm)
Predicted Engine Speed (rpm) .
Calibration <strong>of</strong> the HDM 4 engine
speed model
20
Van
2500
2000
1500
y = 0.0062x 3 - 0.3018x 2 + 6.7795x + 671.98
R 2 = 0.96
2000
1500
1000
500
0
0 20 40 60
Speed (Km/h)
measured engine speed-Wet condition
engine speed model (HDM 4)
measured engine speed-Dry condition
Calibrated model
1000
500
0
0 500 1000 1500 2000
Measured Engine Speed (rpm)
20

Calibrated Fuel rate (mL/Km) .
Calibrated Fuel rate (mL/Km) .
Observed fuel consumption versus
estimated after calibration
21
100
80
y = x
R 2 = 0.90
SSE = 4.09
120
100
y = x
R 2 = 0.89
SSE = 4.19
60
40
80
60
40
20
20
0
0 20 40 60 80 100
0
0 20 40 60 80 100 120
Measured Fuel rate (mL/Km)
Measured Fuel rate (mL/Km)
Passenger car
SUV
21

Calibrated Fuel rate (mL/Km) .
Calibrated Fuel rate (mL/Km) .
Calibrated Fuel rate (mL/Km) .
200
150
y = x
R 2 = 0.83
SSE = 9.58
300
250
200
y = x
R 2 = 0.82
SSE = 10.16
22
100
150
50
100
50
0
0 50 100 150 200
0
0 50 100 150 200 250 300
Measured Fuel rate (mL/Km)
Measured Fuel rate (mL/Km)
Van
Light truck
400
300
y = x
R 2 = 0.88
SSE = 5.29
200
100
0
0 100 200 300 400
Measured Fuel rate (mL/Km)
Articulated truck
22

23
Heavy Truck: Analysis <strong>of</strong>
covariance at 55 mph
23

24
Heavy truck: Analysis <strong>of</strong> covariance
at 35 mph
24

Change in fuel consumption (%) .
Change in fuel consumption (%) .
<strong>Effect</strong> <strong>of</strong> roughness:
HDM 4 versus regression data
25
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
1 2 3 4 5
IRI (m/km)
Medium car
SUV
Van
Light truck
Articulated truck
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
1 2 3 4 5
IRI (m/km)
Medium car- HDM 4
SUV - HDM 4
Van - HDM 4
Light truck - HDM 4
Articulated truck - HDM 4
Medium car - Regression
SUV - Regression
Van - Regression
Light truck - Regression
Articulated truck - Regression
Before calibration
After calibration
25

Change in fuel consumption (%) .
<strong>Effect</strong> <strong>of</strong> Texture on Fuel Consumption -
Regression
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0 0.5 1 1.5 2 2.5 3
MPD (mm)
Medium car - 35 mph
SUV - 35 mph
Van - 35 mph
Light truck - 35 mph
Articulated truck - 35 mph
Medium car - 55 mph
SUV - 55 mph
Van - 55 mph
Light truck - 55 mph
Articulated truck - 55 mph
26

27
<strong>Effect</strong> <strong>of</strong> pavement type on fuel
consumption
• Conduct univariate analysis having IRI as
a covariate and pavement type as fixed
factor
• Repeat the analysis for 35, 45 and 55 mph
27

<strong>Effect</strong> <strong>of</strong> pavement type on fuel
consumption
Summer
Winter
Sig. Not Sig. Sig. Not Sig.
Passenger Car √ √
VAN √ √
SUV √ √
Light Truck √* √ † √
Articulated Truck √* √ † √
* Trucks driven over AC at 35 mph consumes more than trucks
driven over PCC
28
† not significant at 45 and 55 mph
28

29
Articulated truck
29

30
Part I
Summary and conclusions
• Field tests as part <strong>of</strong> NCHRP 1-45 confirmed the
effect <strong>of</strong> roughness on fuel consumption and allowed
for calibration and validation <strong>of</strong> the HDM 4 FC
model.
• <strong>Effect</strong> <strong>of</strong> texture depth on fuel consumption could
only be seen for heavy truck at low speed (35 mph)
• <strong>Effect</strong> <strong>of</strong> pavement type could only be seen in
summer conditions, only for trucks and only at low
speed (35 mph)
30

Part II:
<strong>Effect</strong> <strong>of</strong> Roughness on Repair and
Maintenance Costs
31

32
HDM 4 Repair and Maintenance Model
• HDM4 Repair and Maintenance Cost model
is empirical.
• HDM-4 model was calibrated using data
from developing countries (e.g., Brazil,
India).
– Labor hours are much higher than in the US
– The inflation in the parts and vehicle prices
between the US and developing countries.
32

HDM 4 repair and maintenance costs
• Parts consumption
model
kp
pc 0 1 pc
PARTS = K0 CKM (a + a RI) + K1 1 + CPCON dFUEL
RI
a
a
a
a
4
5
5
7
max
IRI 0 3
IRI 0 3
IRI 0 a
IRI 0
IRI 0
a
7
a
7
IRI 0
IRI ,min
a7
7
IRI
0
, a4
a5
*
IRI
a6
33
Smoothing equation
• Labor hours
lh
a3
a
2
PARTS
K
lh
LH K0
1
33

R&M costs ($)
34
Updating Zaniewski’s tables
Average
Roughness
Tables
and
Charts
Develop Time
Series Trends
Update
1969 1982 2007
Time (years)
Data from DOT fleet
economic analysis
Previous
Tables/Data
34

35
Data Analysis (Empirical approach)
• Repair and maintenance costs from
Texas DOT and Michigan DOT
• Extract only repair costs related to damage
from vibrations:
– Underbody inspection
– Axle repair and replacement
– Shock absorber replacement
35

Parts cost ($)
Parts ($)
Parts cost ($)
Labor costs ($)
Labor costs ($)
Labor costs ($)
Parts Costs ($)
Parts costs ($)
Parts cost ($)
Labor costs ($)
Labor costs ($)
Labor costs ($)
R&M Costs
from MDOT
$800
$600
$500
$400
$400
$300
$200
$200
$100
$0
$0
1.40 1.60 1.80 2.00 2.20 2.40
IRI (m/km)
Parts Labor
(a) Passenger Car
$1,000
$800
$600
$400
$200
$0
1.2 1.7 2.2 2.7
IRI (m/km)
Parts Labor
(b) Light Trucks
36
$500
$400
$300
$200
$100
$0
$1,200
$1,000
$800
$600
$400
$200
$0
1.2 1.7 2.2 2.7
IRI (m/km)
$800
$600
$400
$200
$0
$1,000
$800
$600
$400
$200
$0
1.2 1.7 2.2 2.7
IRI (m/km)
$600
$500
$400
$300
$200
$100
$0
Parts
Labor
Parts
Labor
(c) Medium Trucks
(d) Heavy Trucks
$2,000
$1,500
$1,500
$1,000
$1,000
$500
$500
$0
$0
1.2 1.7 2.2 2.7
IRI (m/km)
$800
$600
$400
$200
$0
1.2 1.7 2.2 2.7
IRI (m/km)
$700
$600
$500
$400
$300
$200
$100
$0
Parts
Labor
Parts
Labor
(e) Articulated Trucks
(f) Buses
36

37
Mechanistic Approach
• A mechanistic-empirical approach was
proposed to conduct fatigue damage analysis
using vehicle-pavement interaction modeling.
37

N
Artificial generation <strong>of</strong> road surface pr<strong>of</strong>ile
38
m
m
Vehicle simulation
Rainflow counting algorithm
Component
properties
stress
Roughness
features
distribution
Vehicle damage models
• Repair cost <strong>of</strong> suspensions
• Typical life <strong>of</strong> suspensions
Additional cost database
38

39
Failure threshold
• User perspective : Replace parts when certain
signs <strong>of</strong> wear become evident.
• Manufacturer lifetime warranty:
– Truck suspensions : 250,000 miles
– Car suspensions : 100,000 miles
39

Probability density function (%) .
40%
29.1%
30%
20.2%
20%
13.6% 14.5%
8.7%
10%
4.3% 3.1% 2.5% 2.3% 1.7%
Multiply the PDF with
250,000 or 100,000 miles
40
0%
1 1.5 2 2.5 3 3.5 4 4.5 5 6
IRI (m/km)
Vehicle miles traveled
over each roughness level
Generate 30 Road Pr<strong>of</strong>iles
for each roughness level
.
.
.
.
.
.
.
.
= Damage threshold
Vehicle simulation
damage analysis
Accumulated damage
caused by each
roughness level
40

Failure threshold (Cont’d)
• For cars: 87.3 %
• For trucks: 62.2 %
• Vehicle manufacturers design their vehicles
for:
– Cars: 90 th to 95 th percentile <strong>of</strong> roughness
– Trucks: 80 th to 95 th percentile <strong>of</strong> roughness
41

Accumulated damage
Probability density function (%) .
For cars
42
1
40%
0.8
84.5%
30%
29.1%
0.6
20%
20.2%
13.6%
14.5%
10%
8.7%
0.4
0%
4.3% 3.1% 2.5% 2.3% 1.7%
1 1.5 2 2.5 3 3.5 4 4.5 5 6
0.2
IRI (m/km)
0
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
IRI (m/km)
3.9
93 rd percentile
Car manufacturers design their vehicle for the 90 th to 95 th
percentile <strong>of</strong> roughness
42

Accumulated damage
Probability density function (%) .
1
For trucks
43
0.8
0.6
66%
40%
30%
20%
29.1%
20.2%
13.6% 14.5%
0.4
10%
8.7%
4.3% 3.1% 2.5% 2.3% 1.7%
0.2
0%
1 1.5 2 2.5 3 3.5 4 4.5 5 6
IRI (m/km)
0
0 0.5 1 1.5 2 2.5 3 3.5
IRI (m/km) 3.2
87 th percentile
Truck manufacturers design their vehicle for the 80 th to 95 th
percentile <strong>of</strong> roughness
43

Accumulated damage
Accumulated damage
Accumulated damage using actual
pr<strong>of</strong>iles from in-service pavements
44
Cars
Trucks
1
0.8
Artificial pr<strong>of</strong>iles
Real pr<strong>of</strong>iles
1
0.8
Artificial pr<strong>of</strong>iles
Real pr<strong>of</strong>iles
0.6
0.6
0.4
0.4
0.2
0.2
0
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
IRI (m/km)
0
0 0.5 1 1.5 2 2.5 3 3.5 4
IRI (m/km)
44

Repair and Maintenance costs ($/1000 km)
Empirical versus mechanistic
predictions: Trucks
300
250
M-E w/ artificial pr<strong>of</strong>iles
45
200
150
Empirical:
Zaniewski/HDM4
100
M-E w/ actual pr<strong>of</strong>iles
50
0
0 1 2 3 4 5 6 7
IRI (m/km)
45

Repair and Maintenance cost ($/1000km)
Empirical versus mechanistic
predictions: Cars
80
75
70
65
60
55
50
45
M-E w/ artificial pr<strong>of</strong>iles
Empirical:
Zaniewski/HDM4
40
35
M-E w/ actual pr<strong>of</strong>iles
30
0 1 2 3 4 5 6 7
IRI (m/km)
46

Total Cost (Cents per Truck)
Example: VOC for Trucks caused by
I69 condition
47
7
6
5
FC
R&M
Total Cost
4
3
2
1
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Distance (miles)
47

Total Cost (Cents per Car)
Example: VOC for Cars caused by
I69 condition
48
1.5
1.4
1.3
1.2
1.1
FC
R&M
Total Cost
1
0.9
0.8
0.7
0.6
0.5
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5
Distance (miles)
48

Thank you!
49