Multi-Objective Optimisation of the Battery Box in a Racing Car

TY - JOUR

T1 - Multi-Objective Optimisation of the Battery Box in a Racing Car

AU - Ma, Chao

AU - Xu, Caiqi

AU - Souri, Mohammad

AU - Hosseinzadeh, Elham

AU - Jabbari, Masoud

N1 - Publisher Copyright: © 2024 by the authors.

PY - 2024/6/25

Y1 - 2024/6/25

N2 - The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures.

AB - The optimisation of electric vehicle battery boxes while preserving their structural performance presents a formidable challenge. Many studies typically involve fewer than 10 design variables in their optimisation processes, a deviation from the reality of battery box design scenarios. The present study, for the first time, attempts to use sensitivity analysis to screen the design variables and achieve an efficient optimisation design with a large number of original design variables. Specifically, the sensitivity analysis method was proposed to screen a certain number of optimisation variables, reducing the computational complexity while ensuring the efficiency of the optimisation process. A combination of the Generalised Regression Neural Network (GRNN) and the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was employed to construct surrogate models and solve the optimisation problem. The optimisation model integrates these techniques to balance structural performance and weight reduction. The optimisation results demonstrate a significant reduction in battery box weight while maintaining structural integrity. Therefore, the proposed approach in this study provides important insights for achieving high-efficiency multi-objective optimisation of battery box structures.

KW - battery box

KW - generalised regression neural network

KW - lightweight design

KW - multi-objective optimisation

KW - non-dominated sorting genetic algorithm II

UR - http://www.scopus.com/inward/record.url?scp=85199875447&partnerID=8YFLogxK

U2 - 10.3390/technologies12070093

DO - 10.3390/technologies12070093

M3 - Article

AN - SCOPUS:85199875447

SN - 2227-7080

VL - 12

JO - Technologies

JF - Technologies

IS - 7

M1 - 93

ER -