Optimizing EV Charging in Battery Swapping Stations with CSO-PSO Hybrid Algorithm

Abstract

Electric vehicles (EVs) and renewable energy sources have gained increasing popularity as people become more concerned about climate change and sustainable energy future. Battery swapping stations are a viable alternative for faster and more convenient EV charging, while B2G technology can help to maintain the grid’s energy input and output. The effective scheduling of charging and battery switching with B2Goperation is a challenging task that necessitates efficient optimization methods. This study proposes a hybrid optimization approach based on cuckoo search and particle swarm optimization (CSO-PSO) for the JCS of EVs using B2G technology in BSS. By establishing the ideal charging and swapping schedule for each EV and optimizing the B2Goperation based on demand and price, the proposed solution intends to increase the efficiency and cost-effectiveness of battery swapping stations. The proposed method has the advantages of attaining faster convergence, more accuracy, and better space solution exploration. The approach’s efficacy and applicability are proved by simulation results, which indicate the algorithm’s efficiency in real time.