基于EGO加点策略的动力电池包多目标优化

For the lightweighting of battery pack and increasing its modal frequency, a multi-objective optimization scheme based on efficient global optimization (EGO) strategy with additive sample points is proposed. Firstly, by using the design of experiment and Pareto rule, the effects of design variables on the optimization objectives are analyzed, and the variables having more significant influences on the mass and 1st-order modal frequency of battery pack are chosen to be optimized so as to reduce the problem-solving difficulty. Then, multi-objective particle swarm optimization (MOPSO) algorithm is adopted assisted with Kriging surrogate model to solve the optimization problem, and the EGO strategy with additive sample points is employed to get the new design points and samples respectively, with the surrogate model updated until the optimization procedure converges. Finally, the test functions are utilized to verify the effectiveness of the scheme proposed, which is then applied to the multi-objective optimization of battery pack. The results show the scheme is efficient and feasible, with which the mass of battery pack reduces by 4.89 kg while maintaining a higher 1st order modal frequency.