Multi-objectives robust optimization based on Grey-Taguchi method for the electric vehicle frame collision

Abstract

The proportion of electric vehicles in vehicle manufacturing is increasing, but with the endurance mileage of the battery improved, the collision safety problem of electric vehicles is becoming more prominent. To solve a multi-objective robust design problem with discrete variables for vehicle collision design, we propose the Grey-Taguchi robust optimization method. The Grey-Taguchi method transforms the multi-objective functions into a single grey relation grade sequence instead of the signal-noises ratio used in Taguchi and selects the optimal combination of design variables predicted by the minimum design of experimental and the analysis of means. For the vehicle crashworthiness problem, the Grey-Taguchi method can converge to the Pareto front with several iterations. The mass of frame, maximum lateral intrusion, and peak deceleration after the optimization are decreased 14.5 %, 32.3 %, and 17.8 %, respectively. The Grey-Taguchi robust optimization method can not only enhance the robustness of the optimization design but also improve the lightweight design performance and crashworthiness. The proposed method is considered promising for complex engineering design problems with multi-objectives and discrete design variables.