Maximizing Photovoltaic Power Output in Partial Shading Conditions for Electric Vehicle Applications: A Comparative Study of Intelligent Control Strategies

The increasing demand for sustainable transportation has positioned Electric Vehicles (EVs) as a key application area for photovoltaic (PV) systems.This study aims to enhance PV performance under Partial Shading Conditions (PSCs) by employing advanced Maximum Power Point Tracking (MPPT) algorithms.A comprehensive simulation model featuring a boost converter and battery integration is developed to evaluate and compare three MPPT techniques: the conventional Perturb and Observe (P&O) method, Grey Wolf Optimization (GWO), and Fuzzy Logic Control.Unlike previous studies, this work conducts a structured and comparative performance analysis across four distinct and progressively complex shading scenarios.Performance metrics include average power output, convergence time, and steady-state oscillations.Simulation results under shading patterns SP1 to SP4 indicate that the Fuzzy Logic approach achieves superior performance, with mean power outputs of 997.094W (SP1) and 410.081W (SP4), outperforming GWO by 1.7% and 0.7%, respectively.These findings offer quantitative evidence for the effectiveness of intelligent MPPT methods in enabling robust and efficient PV integration for EV applications.