Multi-objective optimization of aluminum foam-filled battery boxes for electric vehicle safety

Abstract

In this study, a multi-objective optimization methodology is used to assess the crashworthiness of an aluminum foam-filled battery box designed for passenger cars. Unlike most research focusing on axial crushing, this work investigates the less-explored side pole impact scenario in electric vehicle battery boxes. Finite element simulations are conducted to reduce peak crushing force (PCF) and increase specific energy absorption (SEA) compared to the initial design. Key design variables include aluminum foam densities, wall thickness, and cross-sectional dimensions of battery box components. Four surrogate models are evaluated to approximate the simulation results, and the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is employed to achieve optimal outcomes. The results show that the optimized design significantly improves crashworthiness, achieving a 50.71% increase in SEA and an 11.56% reduction in PCF. Foam density plays a crucial role in controlling deformation behavior under impact conditions. These findings offer a new approach to designing battery boxes with enhanced crashworthiness for electric vehicles.