Publication

Abstract : The hybrid EV charging system integrates solar photovoltaic (PV), battery storage, and the grid to provide consistent and sustainable charging. In order to achieve optimum performance, these hybrid EV charging stations need to employ an efficient maximum power point tracking (MPPT) system. In this paper, the techniques of Cuckoo Search Optimization (CSO) and Grey Wolf Optimization (GWO) are analyzed for their effectiveness at extracting maximum power under different environmental conditions. In a hybrid EV charging system with uniform as well as diverse complex partial shading conditions, these algorithms are evaluated for power extraction efficiency, tracking speed, and stability. Simulation results demonstrate that while both algorithms enhance power quality, GWO consistently outperforms CSO by achieving faster convergence, reduced power oscillations, and improved accuracy in tracking global maximum power point. This study contributes to developing smarter and more effective MPPT systems for hybrid electric vehicle charging infrastructure.