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

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6.1.1 Spatial Information Using the spatial location provided by the on-board GPS (i.e., latitude and longitude collected every 0.2 seconds), the ORNL-developed software determined the road on which the truck was traveling (freeway, arterial, other), if it was traveling inside or outside an urban area, as well as the state in which the truck was located. For the latter (state and urban/rural areas), a database with the perimeter (defined by a series of lat-long points) of each one of the 500+ urban areas in the U.S., and each one of the lower 48 states was created with information obtained from the U.S. Census Bureau. This database was used to determine, at any instance, the names of the urban area and state in which the truck was located, or just the name of the state if it was traveling in a rural area. This spatial information was added to the database containing the 60 channels collected by the on-board equipment. Much more challenging was the determination of the road on which the truck was traveling, especially since databases with this type of information are not readily available. The first approach was to use the spatial data collected by the trucks, and by using a GIS software package (Microsoft MapPoint), to visually determine the type of road (freeway, surface street) as well as the name of the road (if it were a freeway). The approach used was to divide the area traveled by all of the trucks during the data collection period (about 13 months) into a grid containing one-degree squares. As the trucks traveled any particular grid, a database with road location (i.e., a series of latitude-longitude points gathered by the GPS system) and roadway name (obtained visually from the map used by the GIS system) was created. Once all of the segments of freeway inside that particular grid were covered, the grid was classified as “complete” and could be utilized to automatically determine (i.e., no longer manually, but by using software) if a truck was traveling on freeways or surface streets when it was located inside that grid. Fig. 55 shows the 350+ one-degree grids in which the traveled area was divided. Fig. 55. One-Degree Wide Grids for Roadway Identification 64
This methodology worked well at the beginning of the data collection when a only few of the grids were traveled; but it became more time consuming as the size of the database increased, and a new approach had to be used. The new approach consisted of the development of a reverse geocoding procedure that used Microsoft MapPoint as the GIS software. In a normal geocoding procedure, a given spatial feature (e.g., a segment of roadway) is represented by a series of points defined by latitude, longitude, and sometimes altitude; and the attributes of that spatial feature (e.g., the name of the roadway, its classification, number of lanes, etc.) that are associated to these points. This information, kept in proprietary databases, is used by GIS software (MapPoint in this case) to draw these features on a map such that when a coordinate is entered (e.g., the location of a truck as determined by the latitude and longitude provided by the on-board GPS device) it could be displayed by placing a symbol of some sort on its actual location (e.g., the offramp from eastbound I-40 at Exit 383 in Knoxville, Tennessee). A reverse geocoding procedure is the process of finding the attributes of a certain feature given the latitude and longitude information of a given point within that feature. That is, and for this project, it was necessary to find the classification and name of a given roadway once a point on, or very close to, that roadway was known. These points were obtained from the on-board GPS devices as the trucks traveled across the country and therefore were known. Software was written to force MapPoint to return the name and classification of a given spatial feature (roadways in this case) when a latitude-longitude point was provided. The information collected in this way was added to the database of channels, data errors, and spatial information (city and state) described above. At this point, the collected information database was archived and three copies were made which were stored at three different locations to minimize the risk of data loss. 6.1.2 Tractor and Trailer Tire Information One important objective of this project was to study the impact of different types of tires on the fuel economy of Class-8 trucks. The type of tires mounted on the six tractors was, of course, known with certainty at any time during the data collection period. On the other hand, the type of tires mounted on the trailer was only directly known when one of the ten instrumented trailers was mated with the participating tractors. For these ten instrumented trailers, the AirWeigh device mounted on each of these trailers (which was used to determine the trailer’s weight in real-time) broadcast a unique ID that was gathered by the DAS and saved as one of the 60 channels. Since that ID was associated uniquely with that particular trailer, the type of tires mounted on the trailer was also known. However, because Schrader had more than 180 trailers, and only ten of those were instrumented, most of the time6 the instrumented tractors were mated with non-instrumented trailers. For these cases, just by looking at the collected data it was impossible to know by the on-board instrumentation which trailer was mated with the tractor (i.e., the channel corresponding to the trailer AirWeigh ID would post a missing value in the database), and as a result the type of tires on that trailer could not be readily determined. It should be noted that Schrader had a mix of tractor and trailers with duals and NGSWBTs. 6 Most U.S. trucking companies utilize their trailers on an availability basis; that is, available trailers are loaded and then the tractor and driver are assigned to a trip as well as to the corresponding trailer. Within this operational protocol, it is very difficult to try to match a particular trailer to a particular tractor. For the numbers of tractors and trailers of Schrader Trucking Company, if the trailers were assigned randomly to the tractors, then it would be expected that tractors would be mated with non-instrumented trailers 94.44% of the time. The observed overall percentage obtained from the collected data was 94.31%, indicating that the trailers were assigned randomly to the tractors. 65
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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
Page 34 and 35: Fig. 10. NGK NOx/O2 Sensors Fig. 11
Page 36 and 37: 1.3.4 De-instrumentation: Following
Page 38 and 39: Fig. 16. Pilot Test North-South Rou
Page 40 and 41: GPS information collected with the
Page 42 and 43: 2.1 REVIEW OF PERFORMANCE MEASURE R
Page 44 and 45: 2.3.2 Performance Measures Fig. 18.
Page 46 and 47: Signal Sensor 53 Road Grade Calcula
Page 48 and 49: 2.3.6 Tractor and Trailer Details F
Page 50 and 51: determined that the particular CANo
Page 52 and 53: Cable runs to and from sensors were
Page 54 and 55: 2.8 CONDUCTION OF FOT The FOT laste
Page 57 and 58: 3. INTERNET DATA ACCESS TOOL The HT
Page 59: Fig. 30. Search Results The file se
Page 62 and 63: When the button “Extract Data”
Page 64 and 65: Data Fig. 33. Segment Merging In ad
Page 66 and 67: Fig. 36 shows the synthetic cycle g
Page 68 and 69: Fig. 39. A Synthetic Duty Cycle wit
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Page 72 and 73: vehicle was carrying a heavy load o
Page 74 and 75: 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 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
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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
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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
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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
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1.0 BACKGROUND 2.0 PROJECT OVERVIEW
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ACKNOWLEDGEMENTS The authors wish t
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evaluate the data collection system
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performance of their next generatio
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March and provided to the ORNL Prog
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Between January, 2005 and May, 2005
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Figure 4: NGK Sensor Figure 5: Omeg
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o Date and Time o Wind Speed o Wind
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Figures 28 through 31 show the ball
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Figure 22: Airweigh System Figure 2
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DVD. Figures 35 and 36 show some of
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3.3.7.1 Pre-Analysis Efforts: As th
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Task 1: Identification of Fleet(s)
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Data # Performance Measure Performa
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Sensor or eDaq Performance Performa
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45 46 47 48 49 50 51 52 53 Measure
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Data # 62 63 64 65-67 68-69 70-71 7
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Data # Performance Measure 87 Headi
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Appendix A-B Final Pilot Test Plan
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• 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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