Emissions Modeling of Heavy-Duty Conventional and Hybrid Electric Vehicles

Today's computer-based vehicle operation simulators use engine speed, engine torque, and lookup tables to predict emissions during a driving simulation [1]. This approach is used primarily for light and medium-duty vehicles, with large discrepancies inherently due to the lack of transient engine emissions data and inaccurate emissions prediction methods [2]. West Virginia University (WVU) has developed an artificial neural network (ANN) based emissions model for incorporation into the ADvanced VehIcle SimulatOR (ADVISOR) software package developed by the National Renewable Energy Laboratory (NREL). Transient engine dynamometer tests were conducted to obtain training data for the ANN. The ANN was trained to predict carbon dioxide (CO₂) and oxides of nitrogen (NOx) emissions based on engine speed, torque, and their representative first and second derivatives over various time ranges. Conventional and hybrid vehicles were simulated in ADVISOR and the vehicle's emissions were predicted by the ANN emissions model based on simulated engine speed and torque. The results were compared with actual emissions data obtained from similar vehicles exercised through transient chassis tests conducted by the WVU Transportable Heavy-Duty Vehicle Emissions Testing Laboratory (THDVETL). The emissions predicted by the ANN model combined with ADVISOR showed good correlation with the emission data from the chassis tests.