Lightweight and crashworthiness design of an electric vehicle using a six-sigma robust design optimization method

Abstract

Design optimization plays an important role in electric vehicle (EV) design. However, fluctuations in design variables and noise factors during the forming process affect the stability of optimization results. This study uses six-sigma robust design optimization to explore the lightweight design and crashworthiness of EVs with uncertainty. A full-scale finite element model of an EV is established. Then, multi-objective design optimization is performed by integrating optimal Latin hypercube sampling, radial basis functions and non-dominated sorting genetic algorithm-II to achieve minimum peak acceleration and mass. Finally, six-sigma robust optimization designs are applied to improve the reliability and sigma level. Robust optimization using adaptive importance sampling is shown to be more efficient than that using Monte Carlo sampling. Moreover, deformation of the battery compartment and peak acceleration of the B-pillar are greatly decreased. The EV’s safety performance is improved and the lightweight effect is remarkable, validating the strong engineering practicability of the method.

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