Metaheuristic driven MPPT intelligence for unified PV battery EV energy convergence

This paper discusses the challenge of reliable maximum power point tracking (MPPT) in solar photovoltaic (PV) battery electric vehicle (EV) charging systems, which face rapidly changing irradiance and temperature. Unlike standard MPPT studies that focus only on standalone PV systems, this work examines MPPT performance within an integrated PV battery grid EV charging framework. It implements and evaluates three recent metaheuristic algorithms: Alibaba Forty Thieves (ABFT), Beluga Whale Optimization (BWO) and Cuckoo Search Optimization (CSO) under the same dynamic operating conditions. A detailed MATLAB/Simulink-based system model includes PV generation, DC–DC boost conversion, bidirectional energy flow, battery management and grid interaction. This model assesses convergence speed, steady-state oscillation, tracking efficiency and energy delivery stability. Simulation results show that the ABFT-based MPPT achieves faster convergence, fewer oscillations and higher energy extraction efficiency compared to BWO and CSO, particularly during sudden changes in irradiance and temperature. The findings suggest that ABFT is a strong MPPT strategy for practical EV charging applications. It offers better energy use, stable battery charging and improved grid interaction under real-world conditions. ABFT reached faster convergence, around 2 to 3 iterations. It also reduced steady-state oscillations and improved energy extraction efficiency when compared to BWO and CSO under the same dynamic conditions.