OPTIMIZED POWER FLOW IN SOLAR PV-GRID INTEGRATED EV CHARGING INFRASTRUCTURE USING FUZZY LOGIC MPPT CONTROL

Abstract

This study offers a detailed simulation of a grid-connected solar PV system with an EV charging station. It focuses on optimizing energy use in real time using a Fuzzy Logic-based Maximum Power Point Tracking (MPPT) control strategy. The main goal is to make the most of solar energy. We want to charge EV batteries without interruption, even when sunlight changes. The proposed system controls power flow between the PV array, the utility grid, and the EV bat-tery. This reduces reliance on grid power and boosts system resilience. The simulation was implemented in MATLAB/Simulink. It modeled a 2 kW PV array in standard test conditions. When irradiance falls and PV output drops to 500 W, the system takes extra power from the grid. This meets the EV's steady charging need of 2000 W. When the sunlight improves, the PV system returns to full output. Any excess en-ergy is sent back to the grid when the EV battery reaches 95% SOC. The DC bus voltage remains steady at 400 V during the operation. This helps power sources switch smoothly and keeps the system running well. The results show that the Fuzzy MPPT controller quickly responds to changes in the environment. It tracks maximum power effectively and boosts energy efficiency more than traditional methods. This hybrid control framework helps set up smart and sustainable EV charging stations. It uses renewable energy and reduces stress on the grid. Plus, it lowers carbon emissions. The find-ings support global sustainability goals. They also offer a model for adding renewable technologies to future trans-porta-tion systems.