Energy optimization of an electric vehicle charging station using a hybrid STA-GWO MPPT strategy

Abstract

This paper presents a hybrid electric vehicle charging station powered by both a PV source and the utility grid, incorporating an energy management strategy that prioritizes the utilization of solar energy while exporting surplus power to the grid during periods of low charging demand. To enhance the performance of maximum power point tracking, a hybrid control strategy integrating the grey wolf optimizer (GWO) and the super-twisting algorithm (STA) is proposed. The GWO performs rapid global exploration to accurately identify the maximum power point, whereas the STA ensures precise, robust, and chattering-free tracking under steady-state operating conditions. The proposed system was modeled in MATLAB/Simulink and validated under a dynamic irradiance profile characterized by both abrupt and gradual variations. Simulation results demonstrate a convergence time of 2-3 ms, residual power oscillations below 0.1%, and an average tracking efficiency of 99.38%. Compared with conventional MPPT techniques, the proposed STA-GWO approach significantly suppresses steady-state oscillations, accelerates convergence, and prevents MPP tracking failure under rapid irradiance fluctuations through the global optimization capability of GWO. These findings highlight the effectiveness of the proposed hybrid MPPT strategy in improving the robustness, energy conversion efficiency, and grid integration capability of PV-powered EV charging stations, making it a promising solution for next-generation sustainable charging infrastructure.