A Comparative Study Of Optimization Techniques For Energy Storage System Sizing In Electric Vehicles

Abstract

Economic efficient energy storage systems serve as an essential requirement enabled by the fast spreading of electric vehicles. This paper delivers an exhaustive evaluation of optimization techniques which determine ESS sizing for EVs through methodologies developed from 2019 to 2024. This research examines five important research papers which demonstrate genetic algorithms together with particle swarm optimization and convex optimization and machine learning-based approaches. A thorough investigation presents an assessment of optimization methods along with their functional boundaries and applicability areas through supporting tabulated data. The proposed optimization method adopts multi-objective optimization techniques to combine them with machine learning algorithms to enhance ESS sizing results. The proposed technique is explained through mathematical representations and schematic drawings. The last part of this paper examines future research pathways which highlight the addition of automated real-time data processing and adaptive programming approaches alongside sustainability elements. The proposed research work helps shape the development of ESS systems for future EV technology.