Design and Optimization of Hybrid Electric Vehicle Drivetrain and Control Strategy Parameters Using Evolutionary Algorithms

Complies with the regulations of this University and meets the accepted standards with respect to originality and quality. Advanced propulsion technologies such as hybrid electric vehicles (HEVs) have demonstrated improved fuel economy with lower emissions compared to conventional vehicles. Superior HEV performance in terms of higher fuel economy and lower emissions, with satisfaction of driving performance, necessitates a careful balance of drivetrain component design as well as control strategy parameter monitoring and tuning. In this thesis, an evolutionary global optimization-based derivative-free, multi-objective genetic algorithm (MOGA) is proposed, to optimize the component sizing of a NOVA ® parallel hybrid electric transit bus drivetrain. In addition, the proposed technique has been extended to the design of an optimal supervisory control strategy for effective on-board energy management. The proposed technique helps find practical trade off-solutions for the objectives. Simulation test results depict the tremendous potential of the proposed optimization technique in terms of improved fuel economy and lower emissions (nitrous-oxide, NOx, carbon monoxide, CO, and hydrocarbons, HC). The tests were conducted under varying drive cycles and control strategies. iv ACKNOWLEDGMENTS