Multi-performance Target Collaborative Optimization Methods for Battery Electric Vehicle

The present studies on battery electric vehicles (BEVs) has mainly focused on the single-objective or weighted multi-objective optimization based on energy management, which can not manifest the coupling relationship among the vehicle performance objectives essentially. To optimize the handling stability, ride comfort and economy of BEV, this paper built the stability dynamics analysis model, ride comfort simulation half-car model and power consumption calculation model of BEV, as well as two-point virtual random excitation model on Level B road and proposed related evaluation indexes, including vehicle handling stability factor, weighted acceleration root-mean-square (RMS) value of vertical vibration at the driver’s seat and power consumption per 100 m at a constant speed. The Pareto optimum principle–based multi-objective evolutionary algorithm (MOEA) of BEV was also designed, which was encoded with real numbers and obtained the target values of all optional schemes via MATLAB/Simulink simulation software. The merits and demerits of alternative schemes could be judged according to the Pareto dominance principle, so that alternative schemes obtained after optimization were realizable. The results of simulation experiment suggest that the proposed algorithm can perform the multi-objective optimization on BEV, and obtain a group of Pareto optimum solutions featured by high handling stability, favorable ride comfort and low energy consumption for the decision-makers.