Hybrid Fuzzy–Multi-Objective Particle Swarm Optimization Control for Real-Time Energy Management in PV-Powered Fast Charging Infrastructure for Electric Vehicles - Elgammal, Adel (Scientific Journal of Engineering Research, 2025)
This paper proposes an innovative Fuzzy–Multi-Objective Particle Swarm Optimization (Fuzzy-MOPSO) based hybrid control strategy for real-time energy management in PV-integrated fast charging systems for EVs. The developed approach combines fuzzy logic control and multi-objective optimization algorithm to achieve dynamic balance between charge rate, power quality, grid stability, and cost of energy. This fuzzy controller can be flexibly used in the presence of variable and uncertainty conditions (e.g., fluctuated solar irradiance, changing EV charging request, grid voltage disturbance) since it has gradual control operations by adjusting converter duty ratios and charging current values. The MOPSO algorithm simultaneously optimizes the multiple antagonistic objectives such as minimization of THD, unity PF with less charging time and increased PV utilization efficiency by adjust fuzzy membership functions and rule weights in real-time. Simulation results in MATLAB/Simulink show that the hybrid controller performs better than classical PI controllers or single fuzzy or PSO based control system. The Fuzzy-MOPSO controller also limits the THD 0.995, and charging efficiency enhancement of (8–12%) with stochastic PV and load changes, in conformity to IEEE-519. Excessively generated energy cost are reduced as well by 15% through the optimal control on the power flow between PV generation, storage and grid. The hybridization of fuzzy reasoning and swarm-based optimization provides for fast transient response, renewable intermittency robustness, and grid integration sustainability. These findings validate that the proposed Fuzzy-MOPSO technique is an appropriate approach to intelligent, efficient and eco-friendly FCI of fast charging in REN smart cities.