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

More documents

Recommendations

Info

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
Page 1 and 2:
Class-8 Heavy Truck Duty Cycle Proj
Page 3:
Vehicle Systems Program ORNL/TM-200
Page 6 and 7:
2.7 LAUNCH OF THE FOT (OCTOBER 23,
Page 8 and 9:
40 Connection and Support Structure
Page 11:
LIST OF TABLES Table Page 1 Pilot T
Page 15:
ACKNOWLEDGEMENTS The Oak Ridge Nati
Page 18 and 19:
Sixty channels of data were collect
Page 20 and 21:
xviii
Page 22 and 23:
The data analyses performed in Phas
Page 24 and 25:
Fig. 2. HHDDT Transient Mode In add
Page 26 and 27:
vehicle; 3) a transit bus; 4) a nei
Page 28 and 29:
payload and accessory load would al
Page 30 and 31:
Data # Performance Measure Benefit
Page 32 and 33:
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
Page 70 and 71: tractors) as well as the maximum to
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
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
Page 118 and 119: analysis controlling for the type o
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 126 and 127: know if the vehicle was “filled u
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
Page 176 and 177:
9 10 11 12 13 14 15 Lateral Velocit
Page 178 and 179:
34 Rain Intensity 35 36 37 38 39 40
Page 180 and 181:
59 60 61 62 63 64 65-67 68-69 70-71
Page 182 and 183:
85 Longitude 86 Velocity 87 Heading
Page 184 and 185:
3.2 Sensors The PMs listed in Table
Page 186 and 187:
vehicles will be dedicated to the p
Page 188 and 189:
DATE TEST ENGINEER: DRIVER: DEPARTI
Page 190 and 191:
Jim Ridge, Dana Product Engineer, w
Page 192 and 193:
Table 5.1 October Route and Schedul
Page 194 and 195:
Table 5.2 December Route and Schedu
Page 196 and 197:
Appendix A-C Field Test Signals 64
Page 198 and 199:
9 Performance Measure Performance M
Page 200 and 201:
Performance Measure 25 Rain Duratio
Page 202 and 203:
Letter Report SUMMARY OF RELEVANT H
Page 204 and 205:
Sensor or Performance Data Sensor o
Page 206 and 207:
GAWR 44 lb AirWeigh 5800 Road Surfa
Page 211:
Appendix B SCHRADER MEMORANDUM OF U
Page 214 and 215:
MOU-UTB-2007001 Schrader Trucking C
Page 216 and 217:
MOU-UTB-2007001 Schrader Trucking C
Page 219:
Appendix C FIELD OPERATIONAL TEST P
Page 222 and 223:
Collection, Analysis, and Archiving
Page 224 and 225:
4.0 APPROACH Phases 2 and 3 of the
Page 226 and 227:
Signal Sensor 60 Tractor-Trailer Ma
Page 228 and 229:
Schrader Trucking will provide six
Page 230 and 231:
Sensitive Information] [Partner Sen
Page 232 and 233:
4.6 Sensor and DAS Integration to T
Page 234 and 235:
5.5 Vehicle Deployment Methodology
Page 236 and 237:
Start address: 2360 Cherahala Blvd
Page 238 and 239:
Figure 6.2: Schrader Shop, Far Left
Page 240 and 241:
Figure 6.7: eDAQ Lite with two Vehi
Page 242 and 243:
6.5 Safety Information For Student
Page 245:
Appendix D SYSTEM DESIGN AND OPERAT
Page 248 and 249:
TABLE OF CONTENTS Collection, Analy
Page 250 and 251:
9.1.2. Shutdown Procedure 9.1.3. SI
Page 252 and 253:
Signal Sensor 26 Cruise Control Acc
Page 254 and 255:
Figure 4.1: Air-Weigh System Compon
Page 256 and 257:
4.5. EDAQ The eDAQ Lite is connecte
Page 258 and 259:
the equipment inside from overheati
Page 260 and 261:
5.4. Weather Station Connections Fi
Page 262 and 263:
Figure 5.5.3: VBOX Cable Table 5.5.
Page 264 and 265:
Figure 6.3: Weather Station Locatio
Page 266 and 267:
6.6 Power Supply Use a second cable
Page 268 and 269:
can also be changed from this windo
Page 270 and 271:
on and the yellow light should begi
Page 272 and 273:
Figure 7.1.3-1: Weather Station and
Page 274 and 275:
Speed unit: m/s Sampling frequency:
Page 276 and 277:
iii) Click on Browse to find and op
Page 278 and 279:
c) Power Controller (Power) - no co
Page 280 and 281:
1) Select the Transducer and Messag
Page 282 and 283:
Figure 7.2.4-2: Adding Transducer C
Page 284 and 285:
Figure 7.2.4-3: Editing DIO Vehicle
Page 286 and 287:
A. Changing Units - Converting tran
Page 288 and 289:
C. Coordinates - Assign correct sig
Page 290 and 291:
switch) Table 7.2.5: Contents of Ti
Page 292 and 293:
Trigger Delay Setup: Check Enable d
Page 294 and 295:
Figure 7.2.5-6: Time Base Shifter S
Page 296 and 297:
Units: Full Scale Estimate - Min: 0
Page 298 and 299:
Figure 7.2.6-2: Burst History Data
Page 300 and 301:
Press Ctrl+3 (or click Run in the T
Page 302 and 303:
8.2. Procedures for Heavy Truck Dut
Page 304 and 305:
d) Turn on the ignition. e) Turn on
Page 306 and 307:
4) Change the IP address field to t
Page 308 and 309:
5) Check to see that the computer c
Page 310 and 311:
e) When all SIC files have been sav
Page 312 and 313:
4) To verify the time setting, go t
Page 314 and 315:
e) Alter the id, mask, value, etc.
Page 316 and 317:
Longitudinal CAN 0x87FF 0x0304 Long
Page 318 and 319:
ULSB 0x98EAFFFBC1FE00 MaxVehSpLm CA
Page 320 and 321:
Figure 9.1.6-2: Performing a ColdSt
Page 322 and 323:
Table 9.2.2-1: Descriptions and Uni
Page 324 and 325:
Channel ID Sensor Format Integer? P
Page 326 and 327:
Channel ID eDAQ Invalid Data Value
Page 328 and 329:
Channel ID Resolution Accuracy Note
Page 330 and 331:
9.3. Appendix C: Additional DAS Not
Page 332 and 333:
e replicated and saved as five iden
Page 334 and 335:
2) Type in “cmd” 3) Type “ipc
Page 336 and 337:
Producing multiple setup files 1) G
Page 338 and 339:
9.4. Appendix D: Checklists and For
Page 340 and 341:
D-96
Page 342 and 343:
9.5. Appendix E: References and Web
Page 345 and 346:
E-3
Page 347 and 348:
E-5
Page 349 and 350:
E-7
Page 351 and 352:
E-9
Page 353 and 354:
E-11
Page 355 and 356:
E-13
Page 357 and 358:
E-15
Page 359 and 360:
E-17
Page 361 and 362:
E-19
Page 363 and 364:
E-21
Page 365 and 366:
E-23
Page 367 and 368:
E-25
Page 369 and 370:
E-27
Page 371 and 372:
E-29
Page 373 and 374:
E-31
Page 375 and 376:
E-33
Page 377 and 378:
E-35
Page 379 and 380:
E-37
Page 381 and 382:
E-39
Page 383:
E-41
show all

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

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?