Optimum design of electric vehicle battery enclosure using the chaotic metaheuristic algorithms

Abstract

Metaheuristic algorithms are optimization techniques inspired by natural processes, widely used to solve complex real-world problems. Traditional methods like swarm-based established optimizers often face challenges like premature convergence and high computational costs. The aim of this research is to develop a new optimization method for optimizing electric vehicle components and real-world problems. This research introduces a new chaotic fishing cat optimization algorithm (CFCO), a new optimization algorithm based on recent fishing cat optimization algorithm and chaotic maps. The chaotic maps are integrated into FCO to improve the balance between exploration and exploitation. This research is the first application of the CFCO to the optimum design of electric vehicle components in the literature. The algorithm is applied to various industrial design optimization problems, including structural optimization of cantilever beams, weight optimization of a coupling with a bolted rim, optimization of side profile of an electric vehicle battery enclosure, and heat exchanger economic optimization. The results demonstrate that the developed CFCO outperforms existing recent metaheuristic techniques, achieving superior efficiency and accuracy in industrial applications.