Performance Analysis of an Anfis Controlled Hybrid Enery System for EV Charging Application

The increasing adoption of electric vehicles (EVs) has created a growing demand for clean, reliable, and cost-efficient charging infrastructure. This paper presents the design and implementation of a Hybrid PV–Wind-Based Electric Vehicle Charging Station integrated with an Adaptive Neuro-Fuzzy Inference System (ANFIS) Energy Management Controller. The proposed system combines solar photovoltaic (PV) and wind energy as primary renewable sources, supported by a battery energy storage system (BESS) to overcome intermittency and ensure continuous power availability.At the core of the system, the ANFIS controller intelligently manages power flow between PV, wind, battery, and grid (optional), based on real-time parameters such as solar irradiance, wind speed, battery state of charge, and EV charging demand. Experimental and simulation results demonstrate that the ANFIS-based EMS improves renewable energy utilization, enhances power stability, reduces grid dependency, and increases charging efficiency. The proposed hybrid renewable EV charging station offers a highly reliable, sustainable, and intelligent solution suitable for modern smart transportation systems.