Hybrid PSO–SSA-based Energy Management Strategy for PV-assisted EV Charging Stations
Abstract
The rapid growth of electric vehicles (EVs) has created significant challenges for the modernization of power systems, particularly in energy management, grid stability, and infrastructure readiness. This article presents a sophisticated energy management system (EMS) intended for a photovoltaic (PV)-battery-electric vehicle (EV) charging station, employing a hybrid Particle Swarm Optimization-Salp Swarm Algorithm (PSO-SSA). It depicts a complex setup that combines PV electricity production, battery storage, EV charging demand, and building consumption under actual operating conditions. The suggested method focuses on improving battery operational behavior (stress, peak power, and smoothness) while maintaining acceptable system reliability. The proposed algorithm aims to enhance the balance between exploration and exploitation, thus allowing for more effective power load coordination. The proposed EMS is evaluated under deterministic operating conditions, and the simulation results are obtained via MATLAB software. The simulation results show that the hybrid PSO-SSA improves battery operation considerably, while full PV utilization (100%) and reliability are kept at the same level. The proposed PSOSSA method reduces battery throughput by 3% and peak discharge power by 29% compared to the baseline strategy. These findings demonstrate that the proposed hybrid PSO-SSA is a robust and effective optimization technique for PV-powered EV charging systems, particularly by mitigating battery degradation and improving long-term operational efficiency.