Firefly Algorithm-Based Parameter Optimization of Fractional-Order PID Controller for Electric Vehicle Engine Charging by Photovoltaic System Integrated with Power Grid and Energy Storage Batteries

This research work proposes an optimization method for determining the parameters of a Fractional-Order Proportional-Integral-Derivative (FOPID) controller for Electric Vehicle (EV) engines. The objective is to minimize the Integral Time Squared Error (ITSE) in order to improve the performance of the EV system when integrated with Photo Voltaic (PV) systems and energy storage batteries, while ensuring seamless power flow management with the power grid. The optimization process employs the Firefly Algorithm (FA), an intelligent optimization technique, to search for the optimal values of the fractional-order PID controller parameters, including the proportional (Kp), integral (Ki), and derivative (Kd) gains. By simulating the integrated system, the performance metrics such as ITSE are evaluated to assess the effectiveness of the optimized FOPID. The results demonstrate that the proposed approach utilizing the FA algorithm successfully minimizes the ITSE, leading to enhanced performance of the EV system. The optimized controller ensures efficient power flow management between the PV system, energy storage batteries, and the power grid, enabling optimal EV charging and discharging operations. This integration contributes to increased energy efficiency, reduced dependence on the power grid, and improved overall system reliability. MATLAB software is used for the simulation.