Advanced vehicle-to-grid control: enhancing energy exchange and power quality with grey wolf optimized bidirectional converters in EV charging infrastructure

This work optimizes the PI controllers of a three-phase bidirectional AC/DC converter to increase Vehicle to-Grid (V2G) system reliability and efficiency. This study aims to solve the limitations of traditional trial-and-error or heuristic tuning methods, which often result in suboptimal performance in dynamic V2G environments. The Grey Wolf Optimiser (GWO) is used to determine optimal controller gains for several objective functions, including Integral Square Error (ISE), Integral Time Absolute Error (ITAE), and Integral Squared Time Error (ISTE). This study shows that a simple, systematically optimised PI controller can compete with more complex techniques in performance. The GWO-tuned controller is closely compared to a standard PI controller and an Adaptive Neuro-Fuzzy Inference System (ANFIS) in MATLAB[Formula: see text] (2023a) under challenging conditions, such as load step changes and sudden power flow reversals. Offline-optimized benefits are confirmed robust by implementing them on a Xilinx Spartan-6 FPGA hardware prototype. Both hardware and simulated results demonstrate the superiority of the GWO-tuned controller. The proposed approach reduces average error reduction by 15%, grid current Total Harmonic Distortion (THD) by 20%, and DC link voltage surge during load transients from 12.5% to 2.3% compared to typical PI controllers. The GWO-PI controller consistently demonstrates improved dynamic response and robustness, proving its suitability for demanding V2G scenarios.