Abstract
This paper presents an adaptive hybrid Maximum Power Point Tracking (MPPT) algorithm developed for high-efficiency solar-powered electric vehicle (EV) charging under variable operating and partial shading conditions. Conventional MPPT algorithms often struggle with partial shading, converging to local power peaks rather than the true global maximum power point (GMPP). To overcome this, the proposed strategy combines adaptive operating-point adjustments with an optimization-based search mechanism to coordinate exploration and exploitation dynamically. The framework evaluates system performance from both a photovoltaic (PV) power extraction perspective and a downstream battery charging requirement lens across a 24-hour cycle. Over 100 iterations, the system achieved fast convergence to an optimal operating voltage of ~20.07 V and a maximum extracted power of ~63.75 W. The charging accuracy rapidly increased from 97.5% to 99.96%, maintaining stability above 99.5% after initial generations. A 24-hour evaluation demonstrated strong agreement between predicted and actual solar power generation profiles, maintaining near-zero percentage errors outside of isolated transient deviations at sunrise and sunset. These findings confirm that the adaptive hybrid MPPT controller ensures rapid voltage stabilization, high-accuracy charging, and effective GMPP tracking for solar EV charging systems.