Comprehensive Analysis of Multi-Objective Optimization Algorithms for Sustainable Hybrid Electric Vehicle Charging Systems

This study presents a multi-objective optimization approach for designing hybrid renewable energy systems electric vehicle (EV) charging stations that considers both economic and reliability factors as well seasonal variations in production consumption. Four algorithms, MOPSO, NSGA-II, NSGA-III, MOEA/D, were evaluated terms of their convergence, diversity, efficiency, robustness. Unlike previous studies focused on single-objective or ignored variations, our results more comprehensive sustainable design EV systems. The proposed system includes 223-kW photovoltaic system, an 80-kW wind turbine, seven Lithium-Ion battery banks, achieving total net present cost USD 564,846, levelized electricity 0.2521 USD/kWh, loss power supply probability 1.21%. NSGA-II outperforms the other algorithms convergence while NSGA-III is most efficient, MOEA/D has highest findings contribute to development efficient reliable urban areas, emphasizing importance considering process. Our represents significant advance field stations.