A HYBRID ANFIS-SMC CONTROL STRATEGY FOR SOLAR-POWERED OFF-GRID ELECTRIC VEHICLE

Abstract

The increasing adoption of electric vehicles (EVs) and the growing demand for sustainable transportation have encouraged the development of renewable energy-based charging infrastructures. Solar-powered off-grid EV charging systems provide an environmentally friendly solution for remote areas and regions with limited grid accessibility. However, the intermittent nature of photovoltaic (PV) generation and the nonlinear characteristics of battery charging necessitate advanced control techniques to ensure efficient energy utilization and stable system operation. This paper proposes a Hybrid Adaptive Neuro-Fuzzy Inference System–Sliding Mode Control (ANFIS-SMC) strategy for a solar-powered off-grid EV charging system. The proposed system integrates a PV array, a battery energy storage system (BESS), a bidirectional DC–DC converter, and an EV charging interface. The ANFIS controller performs intelligent energy management and reference generation, while the Sliding Mode Controller provides robust tracking and fast dynamic response under varying environmental and load conditions. The hybrid approach combines the adaptive learning capability of ANFIS with the robustness of SMC to improve voltage regulation, reduce charging fluctuations, and enhance overall system efficiency. MATLAB/Simulink simulations validate the proposed controller under different irradiance levels, battery states of charge, and EV charging scenarios. The results demonstrate superior performance in terms of settling time, charging stability, energy utilization  efficiency, and robustness compared with conventional PI and standalone fuzzy control methods.