Application of multi-objective genetic algorithm in the structural optimization of battery box for new energy vehicles
Abstract
As the rapid development of new energy vehicles places increasingly stringent requirements on battery safety, lightweight design, and thermal management, the structural optimization of battery boxes has become a critical engineering challenge. To address the conflicting objectives among structural strength, heat dissipation efficiency, and weight reduction, this study proposes a multi-objective optimization method based on a non-dominated sorting genetic algorithm. A parameterized battery box model is established, with mass, maximum deformation, and maximum operating temperature selected as optimization objectives. The optimization framework integrates structural and thermal performance constraints to achieve balanced design solutions. Numerical analysis demonstrates that the optimized battery box significantly improves mechanical strength and thermal management capability while reducing structural weight. The proposed approach effectively resolves complex multi-objective optimization problems and provides theoretical support for the integrated design of battery systems. In addition, the optimization strategy offers methodological references for electromagnetic compatibility considerations, thermal-field coupling analysis, and intelligent design of advanced energy storage systems.