Multi-parameter similarity model-based lightweight design of battery electric vehicle body-in-white

Abstract

The bending stiffness, torsional stiffness, and low-order modal frequencies of the body-in-white (BIW) are crucial factors in structural lightweight design. This paper introduces a BIW optimization approach for electric vehicles (BEVs) that integrates comprehensive sensitivity analysis and multi-parameter similarity models. The aim is to efficiently balance the BIW’s stiffness performance with minimal weight, while also managing potential manufacturing cost increases from design modifications. Firstly, the validity of the computational model is demonstrated by the BIW bending and torsion stiffness test and modal test. On this basis, the integrated sensitivity evaluation indexes of the structural member thickness parameters to the stiffness, weight and low-order modal frequency response of the BIW were calculated. The sheet metal thickness parameters of 17 structural members were preferred as design variables, and a multi-parameter similarity prediction model for BIW weight reduction design was constructed. Minimum weight, maximum stiffness and higher modal frequency of the BIW are defined as the optimization objectives. The optimal solution was finally solved using a multi-objective optimization algorithm. The results indicate a 1.582% reduction in weight of the BIW compared to the pre-optimization BIW. This paper delves into the intricate interplay between the parameters of the BIW’s structural components using simulation optimization techniques alongside experimental validation. This study not only proposes a new idea for the problem of how to efficiently and accurately determine the best optimization object from a large number of structural components in the development of vehicle structural safety, but also provides a research basis for automobile manufacturers to implement further weight reduction and optimization work on the BIW at the later stage of BEV development.