A Novel MPPT Method for Optimizing Wireless Power Transfer in EV Charging Using Integrated Photovoltaic Systems

The rapid adoption of Electric Vehicles (EVs) as sustainable modes of transportation has intensified the demand for efficient and reliable charging infrastructures. While photovoltaic-powered wireless power transfer (PV-WPT) systems have emerged as a promising solution. A significant challenge in these systems is obtaining fast and precise Maximum Power Point Tracking (MPPT), especially under partial shading conditions (PSCs) , where the conventional techniques, such as perturb and observe (P&O), exhibit steady-state oscillations and may not be able to track the global maximum power point (GMPP). Although metaheuristic methods have been developed to overcome these limitations, their convergence decreases significantly when irradiance and load variations occur, due to the utilization of the same re-initialization criteria. To overcome these limitations, this paper proposes an advanced MPPT strategy that integrates a Modified Red-Billed Blue Magpie Optimizer (MRBMO) to enhance convergence speed and global point detection under PSCs. Moreover, a predictive power slope method has been proposed to prevent re-initialization during the load variations. The proposed PV-WPT system achieved an average wireless transfer efficiency of 87.2% and an MPPT efficiency of 99.94%, with an average convergence time of 0.68 s. In addition, the system demonstrated strong resilience under high-power operation and coil misalignment scenarios. Comparative evaluation against recent MPPT algorithms further highlights the superior robustness and effectiveness of the proposed approach.