AI-BASED ADAPTIVE G2V POWER MANAGEMENT CONTROLLER FOR EV CHARGING STATIONS USING ANFIS

Abstract

The rapid proliferation of Electric Vehicles (EVs) poses significant challenges and opportunities for modern power systems. Grid-to-Vehicle (G2V) technologies enable bidirectional energy exchange, allowing EVs to act as distributed energy storage units for grid support. This paper proposes an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control strategy for smart EV charging stations to optimize and G2V operations under varying load and grid conditions. The ANFIS controller adapts to real-time fluctuations in grid voltage, frequency, and EV battery state-of-charge (SOC), ensuring efficient energy flow while maintaining grid stability. Simulation studies demonstrate improved voltage regulation, reduced power losses, and enhanced EV battery utilization compared to conventional PI controllers.