With the advent of hybrid electric vehicles, computerbased 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 heavyduty 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 [CO 2 ] 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 CO 2 and NO x in the case of a class-8 truck but were more accurate as the truck weight increased.

Linear Engine Development for Series Hybrid Electric Vehicles by Csaba Tóth-Nagy This dissertation argues that diminishing oil reserves, concern over global climate change, and desire to improve ambient air quality all demand the development of environment-friendly personal transportation. In certain applications, series hybrid electric vehicles offer an attractive solution to reducing fuel consumption and emissions. Furthermore, linear engines are emerging as a powerplant suited to series HEV applications. In this dissertation, a linear engine/alternator was considered as the auxiliary power unit of a range extender series hybrid electric vehicle. A prototype linear engine/alternator was developed, constructed and tested at West Virginia University. The engine was a 2-stroke, 2-cylinder, dual piston, direct injection, diesel engine. Experiment on the engine was performed to study its behavior. The study variables included mass of the translator, amount of fuel injected, injection timing, load, and stroke with operating frequency and mechanical efficiency as the basis of comparison. The linear engine was analyzed in detail and a simple simulation model was constructed to compare the trends of simulation with the experimental data and to expand on the area where the experimental data were lacking. The simulation was based on a simple and analytical model, rather than a detailed and intensely numerical one. The experimental and theoretical data showed similar trends. Increasing translator mass decreased the operating frequency and increased compression ratio. Larger mass and increased compression ratio improved the ability of the engine to sustain operation and the engine was able to idle on less fuel injected into the cylinder. Increasing the stroke length caused the operating frequency to drop. Increasing fueling or decreasing the load resulted in increased operating frequency. This projects the possibility of using the operating frequency as an input for feedback control of the engine. Injection timing was varied to investigate two different modes of engine operation experimentally. The two modes were direct injection compression ignition (DICI) and "pseudo" homogeneously charged compression ignition (PHCCI). Simulation was performed to include HCCI operation in the study. The study showed that the HCCI operation resulted in higher peak cylinder pressure than that of DICI operation. A combined genetic algorithm-artificial neural network predictor model was used along with the simulation model to find the combination of engine parameters that yielded the highest engine efficiency. The predictor-simulator model suggested the most efficient combination of engine parameters. I thank Dr. Nigel N. Clark for his guidance during my postgraduate studies. Not only he did provide invaluable input to my research, he also showed the way with his integrity, philosophy, and attitude. I also thank Dr.

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