Multi-objective lightweight design of automotive battery pack box for crashworthiness

Abstract

To study an efficient lightweight method of electric vehicle power packs, the paper proposes that a hybrid method is combined with the modified Genetic Algorithm (NSGA-II), the contribution analysis method and the TOPSIS method for improving the battery pack enclosure (BPE) crashworthiness and reducing the structural mass. First, the finite element model of BPE and the crushing crashworthiness model were established and effectively verified by constrained modal analysis and crushing tests. Next, the initially selected optimized components were screened with the contribution analysis method to identify the final optimized components with thickness as the design variable. Also, the battery pack structure values at a design point with the Latin hypercube sampling are obtained. Moreover, the response surface (RSM) agent model was used to construct a relationship between optimization indicators and the design variables. Based on that, this paper carried out the multi-objective optimization design of NSGA-II algorithm and obtained the optimal compromise solution by TOPSIS method. And finally, by numerical simulation, the optimization results were verified and compared with the initially designed BPE. The results showed that the optimized battery pack components reduced the total weight by 4.31% and the crushing deformation of the box by 5.97%.These results contribute to the lightweight and crash-resistant BPE design with excellent performance.

Keywords:

• Battery pack enclosure
• crashworthiness
• NSGA-II algorithm
• multi-objective lightweight design

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by the Key Research and Development Projects in Hubei Province [2020BAB138], supported by High-Performance Computing Center of Wuhan University of Science and Technology, and Hubei Higher Education Research Project [2015233].