Class-8 Heavy Truck Duty Cycle Project Final Report - Center for ...

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know if the vehicle was “filled up” or only partially filled during any given fueling. This uncertainty made it impossible to calibrate the on-board fuel consumption sensor. Lesson Learned: In order to get accurate fuel consumption data and to be able to determine if the on-board fuel consumption sensor can be calibrated, it is recommended that an external fuel sensor be used, if such a sensor can be independently calibrated. This sensor should be deployed for at least 1/3 of the data collection period. If fuel tickets are to be used to calibrate the databus data, then information about fuel time, location, odometer reading, and whether the tank was filled up or not, needs to be collected. 7.2.2.6 Control of experimental variables In this project, an attempt was made to maintain all relevant variables at the same level across all the participating trucks, except for the type of tires and transmission. It was assumed that all drivers were driving in a similar fashion (a reasonable assumption considering the extensive training and follow up that Schrader provides to its drivers), and that other variables that could not be controlled (e.g. vehicle weight, type of terrain, etc) would equally affect all of the vehicles in a similar manner over the yearlong data collection effort. Nevertheless, and to eliminate any potential differences between the participating trucks, it would have been desirable to switch tires among those trucks such that for any given tractor half of the data would have been collected while equipped with dual tires and the other half with the other type of tires . This would have also reduced any influence that drivers may have had in the vehicle fuel efficiency. Lesson Learned: Steps should be taken to eliminate driver, routing, and vehicle variability in the experimental setup in order to reduce the influence of uncontrollable variables. 7.2.2.7 Control of external data gathered It is impossible to collect all of the necessary information for a project like this one by just relying on sensors and other equipment. In order to calibrate some of this information, it was necessary to involve the participating trucking company personnel, specifically the staff dealing with the day-today operations. Even when the staff has the best of intentions in helping the project (as was the case for Schrader), there are operations procedures and idiosyncrasies that simply cannot be changed. One example was the mating of instrumented tractor and trailers, which was impossible to achieve because of the way in which the company operates (i.e., a driver- or tractor-centered operation), even though the staff tried their best to support the project needs. Another example was the collection of fuel tickets. ORNL asked Schrader to collect weight tickets (at random) to help assess and calibrate the Air-Weigh devices. This worked only for a short while, but rapidly reverted back to obtaining weight tickets when the truck was close to the legal weight limit; which is the logical operation for a trucking company. Lesson Learned: Requirements that are outside of normal operations for an operating fleet are likely to be unsustainable in the long-run and can only be maintained for short period of times. If external information needs to be gathered, the methodology to collect that information should either enroll the collaboration of the partner for a short period of time, or it should increase the participation of the research team in collaboration with that partner. Any methodology that requires repetitive tasks that are outside of the partner’s normal way of operating is likely unachievable. 106
7.2.2.8 Database manipulation The project collected a very large amount of information making it difficult to rapidly find and extract segments of data with a given set of attributes. Lesson Learned: It is recommended that similar future projects use commercial database engines (e.g., an SQL Server) to organize and query the extensive databases that are generated in these types of studies. 107
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Class-8 Heavy Truck Duty Cycle Proj
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Vehicle Systems Program ORNL/TM-200
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2.7 LAUNCH OF THE FOT (OCTOBER 23,
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40 Connection and Support Structure
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LIST OF TABLES Table Page 1 Pilot T
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ACKNOWLEDGEMENTS The Oak Ridge Nati
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Sixty channels of data were collect
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xviii
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The data analyses performed in Phas
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Fig. 2. HHDDT Transient Mode In add
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vehicle; 3) a transit bus; 4) a nei
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payload and accessory load would al
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Data # Performance Measure Benefit
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Data # Performance Measure Benefit
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Fig. 10. NGK NOx/O2 Sensors Fig. 11
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1.3.4 De-instrumentation: Following
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Fig. 16. Pilot Test North-South Rou
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GPS information collected with the
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2.1 REVIEW OF PERFORMANCE MEASURE R
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2.3.2 Performance Measures Fig. 18.
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Signal Sensor 53 Road Grade Calcula
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2.3.6 Tractor and Trailer Details F
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determined that the particular CANo
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Cable runs to and from sensors were
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2.8 CONDUCTION OF FOT The FOT laste
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3. INTERNET DATA ACCESS TOOL The HT
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Fig. 30. Search Results The file se
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When the button “Extract Data”
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Data Fig. 33. Segment Merging In ad
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Fig. 36 shows the synthetic cycle g
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Fig. 39. A Synthetic Duty Cycle wit
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tractors) as well as the maximum to
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vehicle was carrying a heavy load o
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Vehicle Speed [mph] Vehicle Speed [
Page 76 and 77: 5.2 REAL-WORLD DUTY CYCLES Informat
Page 78 and 79: Vehicle Speed [mph] 70 60 50 40 30
Page 80 and 81: Given the size and complexity of th
Page 82 and 83: data or out-of-range data. Therefor
Page 84 and 85: 6.1.1 Spatial Information Using the
Page 86 and 87: For those cases in which an instrum
Page 88 and 89: Fig. 56. DCGenT Prototype Search Cr
Page 90 and 91: at constant speed. Parameters m, b,
Page 92 and 93: weight. Notice that, as expected, m
Page 94 and 95: the loss of information, and conseq
Page 96 and 97: consumption per unit of distance tr
Page 98 and 99: q q ( − ) ( feb'− feb) 100 '
Page 100 and 101: Table 15. General Statistics: Total
Page 102 and 103: 70% 60% 50% 40% 30% 20% 10% 0% Idli
Page 104 and 105: Fig. 68. All Trips with Dual Tires
Page 106 and 107: The almost 700,000 miles logged dur
Page 108 and 109: Frequency 300 250 200 150 100 50 0
Page 110 and 111: Speed [mph] 80 70 60 50 40 30 20 10
Page 112 and 113: vehicle is hauling light or heavy l
Page 114 and 115: Frequency 350 300 250 200 150 100 5
Page 116 and 117: A summary of the statistics describ
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Page 120 and 121: 6.3.2.4 Summary of Fuel Efficiency
Page 122 and 123: Lesson Learned: A technology progre
Page 124 and 125: Lesson Learned: Adding external sen
Page 129 and 130: 8. SUMMARY OF RESULTS AND CONCLUSIO
Page 131 and 132: 9. FUTURE DIRECTIONS The Heavy Truc
Page 133: Appendix A PILOT TEST LETTER REPORT
Page 136 and 137: 1.0 BACKGROUND 2.0 PROJECT OVERVIEW
Page 138 and 139: ACKNOWLEDGEMENTS The authors wish t
Page 140 and 141: evaluate the data collection system
Page 142 and 143: performance of their next generatio
Page 144 and 145: March and provided to the ORNL Prog
Page 146 and 147: Between January, 2005 and May, 2005
Page 148 and 149: Figure 4: NGK Sensor Figure 5: Omeg
Page 150 and 151: o Date and Time o Wind Speed o Wind
Page 152 and 153: Figures 28 through 31 show the ball
Page 154 and 155: Figure 22: Airweigh System Figure 2
Page 156 and 157: DVD. Figures 35 and 36 show some of
Page 158 and 159: 3.3.7.1 Pre-Analysis Efforts: As th
Page 160 and 161: Task 1: Identification of Fleet(s)
Page 162 and 163: Data # Performance Measure Performa
Page 164 and 165: Sensor or eDaq Performance Performa
Page 166 and 167: 45 46 47 48 49 50 51 52 53 Measure
Page 168 and 169: Data # 62 63 64 65-67 68-69 70-71 7
Page 170 and 171: Data # Performance Measure 87 Headi
Page 172 and 173: Appendix A-B Final Pilot Test Plan
Page 174 and 175: • DAS/sensor suite/cabling operab
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9 10 11 12 13 14 15 Lateral Velocit
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34 Rain Intensity 35 36 37 38 39 40
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59 60 61 62 63 64 65-67 68-69 70-71
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85 Longitude 86 Velocity 87 Heading
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3.2 Sensors The PMs listed in Table
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vehicles will be dedicated to the p
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DATE TEST ENGINEER: DRIVER: DEPARTI
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Jim Ridge, Dana Product Engineer, w
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Table 5.1 October Route and Schedul
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Table 5.2 December Route and Schedu
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Appendix A-C Field Test Signals 64
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9 Performance Measure Performance M
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Performance Measure 25 Rain Duratio
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Letter Report SUMMARY OF RELEVANT H
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Sensor or Performance Data Sensor o
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GAWR 44 lb AirWeigh 5800 Road Surfa
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Appendix B SCHRADER MEMORANDUM OF U
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MOU-UTB-2007001 Schrader Trucking C
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MOU-UTB-2007001 Schrader Trucking C
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Appendix C FIELD OPERATIONAL TEST P
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Collection, Analysis, and Archiving
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4.0 APPROACH Phases 2 and 3 of the
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Signal Sensor 60 Tractor-Trailer Ma
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Schrader Trucking will provide six
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Sensitive Information] [Partner Sen
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4.6 Sensor and DAS Integration to T
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5.5 Vehicle Deployment Methodology
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Start address: 2360 Cherahala Blvd
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Figure 6.2: Schrader Shop, Far Left
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Figure 6.7: eDAQ Lite with two Vehi
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6.5 Safety Information For Student
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Appendix D SYSTEM DESIGN AND OPERAT
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TABLE OF CONTENTS Collection, Analy
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9.1.2. Shutdown Procedure 9.1.3. SI
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Signal Sensor 26 Cruise Control Acc
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Figure 4.1: Air-Weigh System Compon
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4.5. EDAQ The eDAQ Lite is connecte
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the equipment inside from overheati
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5.4. Weather Station Connections Fi
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Figure 5.5.3: VBOX Cable Table 5.5.
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Figure 6.3: Weather Station Locatio
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6.6 Power Supply Use a second cable
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can also be changed from this windo
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on and the yellow light should begi
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Figure 7.1.3-1: Weather Station and
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Speed unit: m/s Sampling frequency:
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iii) Click on Browse to find and op
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c) Power Controller (Power) - no co
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1) Select the Transducer and Messag
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Figure 7.2.4-2: Adding Transducer C
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Figure 7.2.4-3: Editing DIO Vehicle
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A. Changing Units - Converting tran
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C. Coordinates - Assign correct sig
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switch) Table 7.2.5: Contents of Ti
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Trigger Delay Setup: Check Enable d
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Figure 7.2.5-6: Time Base Shifter S
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Units: Full Scale Estimate - Min: 0
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Figure 7.2.6-2: Burst History Data
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Press Ctrl+3 (or click Run in the T
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8.2. Procedures for Heavy Truck Dut
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d) Turn on the ignition. e) Turn on
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4) Change the IP address field to t
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5) Check to see that the computer c
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e) When all SIC files have been sav
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4) To verify the time setting, go t
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e) Alter the id, mask, value, etc.
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Longitudinal CAN 0x87FF 0x0304 Long
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ULSB 0x98EAFFFBC1FE00 MaxVehSpLm CA
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Figure 9.1.6-2: Performing a ColdSt
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Table 9.2.2-1: Descriptions and Uni
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Channel ID Sensor Format Integer? P
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Channel ID eDAQ Invalid Data Value
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Channel ID Resolution Accuracy Note
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9.3. Appendix C: Additional DAS Not
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e replicated and saved as five iden
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2) Type in “cmd” 3) Type “ipc
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Producing multiple setup files 1) G
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9.4. Appendix D: Checklists and For
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D-96
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9.5. Appendix E: References and Web
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E-3
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E-5
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E-7
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E-9
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E-11
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E-13
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E-15
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E-17
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E-19
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E-21
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E-23
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E-25
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E-27
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E-29
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E-31
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E-33
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E-35
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E-37
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E-39
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E-41
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