title: Further validation of artificial neural network-based emissions simulation models for conventional and hybrid electric vehicles.
abstract: With the advent of hybrid electric vehicles, computer-based vehicle simulation becomes more useful to the engineer and designer trying to optimize the complex combination of control strategy, power plant, drive train, vehicle, and driving conditions. With the desire to incorporate emissions as a design criterion, researchers at West Virginia University have developed artificial neural network (ANN) models for predicting emissions from heavy-duty vehicles. The ANN models were trained on engine and exhaust emissions data collected from transient dynamometer tests of heavy-duty diesel engines then used to predict emissions based on engine speed and torque data from simulated operation of a tractor truck and hybrid electric bus. Simulated vehicle operation was performed with the ADVISOR software package. Predicted emissions (carbon dioxide [CO2] and oxides of nitrogen [NO(x)]) were then compared with actual emissions data collected from chassis dynamometer tests of similar vehicles. This paper expands on previous research to include different driving cycles for the hybrid electric bus and varying weights of the conventional truck. Results showed that different hybrid control strategies had a significant effect on engine behavior (and, thus, emissions) and may affect emissions during different driving cycles. The ANN models underpredicted emissions of CO2 and NO(x) in the case of a class-8 truck but were more accurate as the truck weight increased.
authors.affiliation: Department of Automation and Applied Informatics, College of Engineering, Kecskemét, Hungary.
authors.forename: Csaba
authors.initials: C
authors.lastname: Tóth-Nagy
authors.forename: John J
authors.initials: JJ
authors.lastname: Conley
authors.forename: Ronald P
authors.initials: RP
authors.lastname: Jarrett
authors.forename: Nigel N
authors.initials: NN
authors.lastname: Clark
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D002245:Carbon Dioxide / Q000032:analysis; D003198:Computer Simulation; D011211:Electric Power Supplies; D005742:Gasoline; D018986:Motor Vehicles; D016571:Neural Networks, Computer; D009589:Nitrogen Oxides / Q000032:analysis; D015203:Reproducibility of Results; D001335:Vehicle Emissions / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D005742:Gasoline; D009589:Nitrogen Oxides; D001335:Vehicle Emissions; D002245:Carbon Dioxide