Comparative Analysis of a Hybrid Electric Vehicle Energy Management System Using a Hybrid YSGA-PSO Optimized Fractional-Order PID Controller

Abstract

Hybrid Electric Vehicles (HEVs) offer a practical route to reducing greenhouse gas emissions and dependence on fossil fuels. Their reliability, however, rests on precise management of the battery State of Charge (SOC) under continually fluctuating renewable generation. For SOC regulation in a solar photovoltaic and wind-based hybrid electric vehicle system, this article compares a traditional proportional-integral-derivative (PID) controller and a fractional-order PID (FOPID) controller. A Hybrid Yellow Saddle Goatfish Algorithm–Particle Swarm Optimization (HYSGFA-PSO) technique is proposed to tune the FOPID gains, combining the strong global exploration of the Yellow Saddle Goatfish Algorithm (YSGA) with the fast local exploitation of Particle Swarm Optimization (PSO). The complete system, comprising a PV array, a wind energy conversion system, a bidirectional DC-DC converter, a battery management system and the load, is modelled using standard, MATLAB-implementable equations and simulated in MATLAB/Simulink. Comparative results for voltage, current, power, SOC and optimizer convergence are presented and benchmarked against recent literature (2015–2026). Simulation results demonstrate that the HYSGFA-PSO-tuned FOPID controller reduces settling time by nearly 70%, rise time by 38%, overshoot by close to 88%, and peak time by approximately 23% relative to the conventional PID controller, confirming the superior transient response, robustness and SOC-regulation capability of the proposed approach. In addition, a dedicated case study on the integration of electric vehicles into the power system is presented, in which the impact of EV penetration on the system frequency response and the robustness of the proposed controller under varying charging conditions and ±25% parametric uncertainty are quantified.