Hybrid Ai Algorithms for Real-Time MPPT Control in Electric Vehicle Power Systems

Abstract

The integration of renewable energy sources, particularly photovoltaic (PV) systems, into electric vehicles (EVs) demands efficient maximum power point tracking (MPPT) techniques to optimize energy harvesting and utilization. Conventional MPPT methods, though widely used, often suffer from slow convergence and reduced accuracy under rapidly changing irradiance and load conditions. To address these limitations, this study proposes the implementation of hybrid artificial intelligence (AI) algorithms that combine the strengths of approaches such as neural networks, fuzzy logic, and evolutionary optimization for real-time MPPT control. The hybrid model dynamically adapts to nonlinear PV characteristics, ensuring faster tracking speed, minimal oscillations, and improved energy efficiency compared to traditional standalone techniques. Simulation and experimental validations highlight significant improvements in tracking accuracy, response time, and system stability, demonstrating the suitability of hybrid AI algorithms for next-generation EV power management. The results emphasize the potential of intelligent hybrid controllers in enhancing the sustainability, performance, and reliability of electric vehicle energy systems.