Design and modelling of solar and Hybrid power based EV charging station using ANFIS controller.

As escalation of air pollution and the rising cost of petrol and diesel, individuals are transitioning towards electric vehicles (EVs). This paper presents a novel approach to integrating controls for the purpose of enabling uninterrupted charging in various modes, including islanded, grid-connected, and DG set connected modes. The system under consideration employs a solar photovoltaic (PV) array, a battery-powered energy storage (BPES), a diesel generator (DG), and a grid-power electric vehicle (EV) charging station (CS). In instances where solar energy is accessible, the system will utilise this renewable source of energy to directly charge the electric vehicle. In the absence of solar energy, the system draws power from either the grid or a DG source. The integration of an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller into the system is aimed at achieving efficient and optimal operation. The ANFIS controller has been formulated based on historical data pertaining to solar irradiation, electric vehicle charging patterns, and meteorological forecasts. The findings indicate that the charging station proposed in the study is capable of delivering consistent and environmentally-friendly energy to electric vehicles, while minimising reliance on the diesel generator. The efficacy and dependability of the system are assessed via simulations for performance evaluation purposes. The method’s performance is assessed through the utilisation of Matlab/Simulink software.