Metamodel-Based Multi-Objective Reliable Optimization for Front Structure of Electric Vehicle

In this paper, a multi-objective reliable optimization (MORO) procedure for the front body of an electric vehicle is proposed and compared with determinate multi-objective optimization (DMOO). The energy absorption and peak crash force of the simplified vehicle model under the full-lap frontal impact condition are used as the design objectives, with the weighted sum of the basic frequency, the first-order torsional and bending frequencies of the full-size vehicle model, and the weight of the front body taken as the constraints. The thicknesses of nine components on the front body are defined as design variables, and their geometric tolerances determine the uncertainty factor. The most accurate metamodel using the polynomial response surface, kriging, and a radial basis function is selected to model four design criteria during optimization, allowing the efficiency improvement to be computed. Monte Carlo simulations are adopted to handle the probability constraints, and multi-objective particle swarm optimization is employed as the solver. The MORO results indicate reliability levels of R= 100 %, demonstrating the significant enhancement in reliability of the front body over that given by DMOO, and reliable design schemes and proposals are provided for further study. © 2018, Society of Automotive Engineers of China (SAE-China).