An adaptive BOA-tuned PI controller for DC-link voltage regulation and bidirectional power flow control in EV charging applications

Abstract

This paper presents an advanced grid-connected electric vehicle (EV) charging system incorporating bidirectional battery energy storage and an adaptive butterfly optimization algorithm (ABOA)-tuned PI controller for DC-link voltage regulation and bidirectional power-flow control. The proposed architecture consists of a PWM rectifier for AC/DC conversion with near-unity power factor and reduced input current harmonics. A high-frequency inverter and isolation transformer provide compact and efficient isolated power transfer for EV charging applications. The major contribution of this work is the implementation of an ABOA-tuned PI controller that dynamically optimizes PI gains to maintain stable DC-link voltage under varying load conditions and battery operating states. The adaptive optimization process minimizes voltage error, overshoot, settling time, and ripple, thereby improving transient response and converter performance. MATLAB/Simulink simulation results demonstrate that the proposed controller achieves superior performance compared with conventional PI, PID, fuzzy logic controller, artificial neural network, particle swarm optimization, genetic algorithm, sliding mode control, and model predictive control-based control methods, achieving a settling time of 0.089 s, conversion efficiency of 98.5%, and total harmonic distortion of 0.6%. Hardware implementation using dsPIC30F4011 digital controllers validates the practical feasibility of the proposed system, showing relationship between simulation and experimental results in terms of DC-link stability, bidirectional energy management, and EV charging performance. Overall, the proposed ABOA-tuned PI controller provides a computationally efficient and cost-effective solution for next-generation smart EV charging infrastructures with improved dynamic stability, power quality, and energy efficiency.