PV–GRID HYBRID EV CHARGING STATION USING X-GAIN BOOST CLAMP CONVERTER AND OPTIMIZED RNN-MPPT

Abstract

Electric vehicle (EV) charging infrastructure that incorporates Photovoltaic (PV) electricity provides a feasible technique to lessen dependency on traditional grid power while maintaining clean energy use. In order to provide dependable power distribution under changing operating conditions, this work proposes a hybrid EV charging station architecture to ensure power distribution under varying operating conditions. The system employs XGain Boost Clamp Converter, to enhance voltage from PV subsystem which leads to high step-up ratio while reducing switching stress and conduction losses. An MPPT method based on Alpine Skiing Optimized (ASO) Recurrent Neural Networks (RNNs), which provide quick convergence, fewer oscillations, and more flexibility in response to temperature and irradiance changes, is used to optimize PV energy. The system is integrated with grid where, the energy generated by PV in excess amount is distributed to both the sources. The bidirectional converter results in effective charging and discharging of EV battery. Stable grid interaction and sinusoidal power injection are guaranteed by a threephase Voltage Source Inverter (VSI) through an LC filter on the grid side. Coordinated PI controllers ensure system stability and efficiency by controlling battery current, DC link voltage, and active/reactive power exchange. According to MATLAB simulation, the proposed EV charging station is a good fit for next-generation smart EV charging infrastructures since it achieves high efficiency, enhanced voltage stability, and flexible bidirectional operation.