Expressing cycles and their emissions on the basis of properties and results from other cycles.
A method is proposed to predict vehicle emissions over a driving cycle on the basis of the vehicle's emissions measured over other driving cycles and the properties of these cycles. These properties include average velocity, average inertial power, and average acceleration. This technique was demonstrated and verified using data from the Coordinating Research Council (CRC) E-55/59 emissions inventory program using the statistical properties of the cycles used for measurement in E-55/59. These cycles were Idle mode, Creep mode, Cruise mode, and Transient mode of the 5-Mode CARB H-HDDT, and their intensive properties were average velocity, average acceleration, and average inertial power. The predicted emissions were from the vehicle driven over the U.S. heavy-duty urban dynamometer driving schedule (UDDS). The emissions data were collected from 56 heavy-duty trucks operating at a test weight of 56000 lbs. The predicted emissions data for the UDDS can be expressed as a linear combination of emissions from Idle, Transient, and Cruise modes, and the weighting factors for the linear combination can be determined without prior knowledge of the UDDS emissions themselves. Different combinations of cycles were employed to predict UDDS emissions, and the combination of Idle, Transient, and Cruise modes was found to be the most suitable. For the 56 heavy-duty trucks, the coefficient of determination (R2) in predicting carbon dioxide (CO2) was 0.80, oxides of nitrogen (NOx) was 0.89, and total particulate matter (PM) was 0.71. The average errors between the predicted and measured cycle emissions were 4.2%, 7.8%, and 46.8%, respectively. As with most emissions modeling tools, CO2 and NOx were better predicted than PM. The generic use of the technique was further demonstrated by predicting the emissions expected to arise from operation over the European Transient Cycle (ETC).
Clark
Nigel N
NN
Center for Alternative Fuels, Engines, and Emissions, West Virginia University, Morgantown, West Virginia 26506, USA.
Vora
Kuntal A
KA
Wang
Lijuan
L
Gautam
Mridul
M
Wayne
W Scott
WS
Thompson
Gregory J
GJ
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D005544:Forecasting / Q000379:methods; D008956:Models, Chemical; D014186:Transportation; D001335:Vehicle Emissions / Q000032:analysis*
D000393:Air Pollutants; D001335:Vehicle Emissions