Models for Predicting Transient Heavy Duty Vehicle Emissions 982652
Heavy duty engine emissions represent a significant portion of the mobile source emissions inventory, especially with respect to oxides of nitrogen (NOx) emissions. West Virginia University (WVU) has developed an extensive database of continuous transient gaseous emission levels from a wide range of heavy duty diesel vehicles in field operation. This database was built using the WVU Transportable Heavy Duty Vehicle Emission Testing Laboratories. Transient driving cycles used to generate the continuous data were the Central Business District cycle (CBD), 5-peak WVU test cycle, WVU 5-mile route, and the New York City Bus cycle (NYCB). This paper discusses continuous emissions data from a transit bus and a tractor truck, each of them powered by a Detroit Diesel 6V-92 engine. Simple correlational models were developed to relate instantaneous emissions to instantaneous power at the drivewheels. Emissions time shifting and residence time distribution (dispersion) associated with emissions measurement were addressed. It was noted that inappropriate time alignments could lead to significant errors. Unfortunately, it is not possible to separate engine torque and speed during the chassis testing, since the shift patterns of the vehicle are not known. From the test data for a specific vehicle, emissions were predicted for a bus or a truck from the data bank during one transient test cycle based on the data gathered on a different transient cycle. Simple correlations of instantaneous NOx and CO against axle power were developed. NOx was modeled acceptably in this fashion, and was found to vary in a near-linear fashion with axle power, but for CO, the modeling was found to be less reliable. Ultimately such models can be applied to known vehicle activity cycles to provide an estimate of the vehicle contribution to the emissions inventory. Problems arise when the cycle used to generate the model does not explore the full power range of the vehicle or the operating envelope of the engine, since emissions predictions may have to be extrapolated. This is of concern in predicting emissions that are nonlinear with respect to axle power.