Optimizing Solar Power for Electric Vehicle Charging Stations using Perturb and Observe and Fuzzy Maximum Power Point Tracking Strategies

This research paper investigates the optimization of solar power utilization for electric vehicle (EV) charging stations through the implementation of Perturb and Observe (P-O) and Fuzzy Maximum Power Point Tracking (MPPT) strategies, aiming to reduce carbon emissions and promote the use of renewable energy in transportation. The study focuses on enhancing the efficiency and effectiveness of PV systems in supplying energy to EVs by employing advanced MPPT techniques. The P-O algorithm is widely recognized for its simplicity and effectiveness in tracking the maximum power point of PV panels under varying environmental conditions, while Fuzzy MPPT leverages fuzzy logic for real-time adjustments, improving accuracy and performance in power tracking. Numerical analysis reveals that Fuzzy MPPT achieves significantly higher performance metrics compared to P-O, including a PV power output of 23.57 kW (versus 23.12 kW for P-O), PV voltage of 300 V (compared to 298 V for P-O), PV current of 78.55 A (surpassing P-O's 77.58 A) and DC link voltage of 469 V (outperforming P-O's 460 V). These results demonstrate the superior optimization of power output and voltage-current levels by the Fuzzy MPPT technique, highlighting its higher efficiency compared to P-O in optimizing solar power utilization for sustainable EV charging infrastructure.