Optimization of Parameter Matching of Hybrid Power System for HEV based on NSGA-Ⅱ Algorithm - Deng Tao; Xu Bin; Li Decai; Luo Weixing (Jixie chuandong)
Aiming at the defects of single target for traditional parameter optimization on hybrid power for hybrid electric vehicle（ HEV）,the fuel economy,emission and riding comfort are as the multi-objectives optimization object,and taking the entire dynamic performance is as constraint condition,then the Non-dominated Sorting Genetic Algorithm-Ⅱ（ NSGA-Ⅱ） multi-objectives parameter optimization algorithm is proposed. The optimization results show that under the condition of meeting dynamic performance and good vehicle speed following effect,the average fuel economy increases by 5. 51%,the average emission increases by 14. 86%,the comfort performance meets with the riding requirements,and the maximum charging and discharging current reduces by half. Moreover,a series of global optimal solution of homogeneous distribution are obtained with the proposed algorithm,and the optimization results are satisfactory,which prove the proposed NSGA-Ⅱ multi-objectives parameter optimization algorithm is accurate and feasible.