Optimal sizing and optimization of financial cost for EVs using SPOA-RBFNN technique

Abstract

This study proposes a hybrid technique for optimal sizing and cost optimization of hybrid energy storage systems (HESS) integrated into electric vehicles (EVs). The proposed technique, SPOA-RBFNN, combines a student psychology-based optimization algorithm (SPOA) and a radial-basis function neural network (RBFNN). The study aims to minimize the overall cost of the HESS by evaluating two design variables: the super-capacitor (SC) and battery pack size. SPOA is employed to optimize the hybrid HESS design variables, ensuring efficient exploration of solution spaces. The RBFNN method is then used to predict the relationship between these design variables and the overall cost of the HESS in electric vehicles. The results show that the proposed technique is more effective than existing techniques, with an efficiency of 97.99039% compared to 82.137% for GA and 77.26589% for particle swarm optimization (PSO). This work offers a comprehensive and innovative approach to optimizing HESS sizing in EVs, connecting the gap between performance optimization and financial cost analysis.