Multiobjective reliability-based design optimisation for front structure of an electric vehicle using hybrid metamodel accuracy improvement strategy-based probabilistic sufficiency factor method

The determinate multiobjective optimisation (DMOO) without considering effects of uncertainties on vehicle body design may fail to satisfy the desired property in practice. In this paper, a multiobjective reliability-based design optimisation (MORBDO) procedure is proposed to perform the design for front structure of an electric vehicle. In which, body performances including full-lap frontal crashworthiness, modal characteristic and lightweight level are involved and coordinated, and the thickness of five key components with geometric tolerances are selected as design variables. Probabilistic constraint in MORBDO is addressed by Monte Carlo simulation (MCS) technique-based probabilistic sufficiency factor (PSF) method. To improve the accuracy of optimisation results, a closed-loop system named hybrid metamodel accuracy improvement strategy is presented here by organising adaptive optimum metamodel selection and the max–min distance approach-based new samples addition technique together. The optimisation problem is solved by the multiobjective particle-swarm-optimisation algorithm. The effectiveness of the proposed procedure is certified by successfully obtaining more accurate and reliable alternative optimum schemes in the design for the front body structure in comparison with DMOO, normal PSF method and safety factor method.