Application of Genetic Algorithm in Optimization of Control Strategy for Hybrid Electric Vehicles
Pu Jinhuan Yin Chengliang Zhang Jianwu Shanghai Jiao Tong University, Shanghai, 200030
A new method based on real-coded genetic algorithm (RCGA) was presented for parametric optimization of control strategy for hybrid electric vehicles. A logic threshold control strategy (LTCS) implemented in an existing hybrid car was considered. A set of parameters was adopted in the LTCS to command the operation of the vehicle. The optimal tuning of these parameters was formulated as a constrained nonlinear programming problem with an objective function minimizing both fuel consumption and emissions. A genetic algorithm using steady-state evolution model and real value coding was proposed and applied to find the optimal set of parameters. Experimental results demonstrate effectiveness of the proposed method, which can be used for off-line parametric optimization and thus shorten the time of controller calibration in the real vehicle.