Intelligent Energy Management for EV Charging in Renewable Energy Based Microgrids Using Advanced Hybrid Fuzzy‐PI Controller

ABSTRACT Background : Increasing Electric Vehicle (EV) possession has resulted in an abundance of Charging Stations (CSs), which nurtures load demands and causes grid interruptions in peak hours. By using an effective Energy Management Strategy (EMS), microgrids provide a workable solution to these problems with the electrical distribution infrastructure. DC microgrids powered by renewable energy present a promising alternative, but their efficacy is limited by the fluctuating availability of renewable energy sources (RES) and the erratic demand for EV charging. Therefore, to ensure cost‐effective, reliable, and environmentally sustainable EV charging, an efficient and adaptive EMS is required. Methods : This research proposes an advanced hybrid energy management approach for DC microgrids powered by RES that incorporate EV charging stations. The method optimises power flow among solar systems, fuel cells, battery storage, and EV loads by combining a Dwarf Mongoose–Zebra Optimisation tuned Proportional–Integral controller with Fuzzy Logic Control (DMZO‐PI+Fuzzy). The hybrid Dwarf Mongoose–Zebra Optimisation algorithm is utilised to optimise PI controller gains. The control signal from the fuzzy control and the DMZO Optimised PI controller are combined to enhance the controller performance in the proposed model of EVCS. MATLAB/Simulink simulations are used to validate the proposed DMZO‐PI+Fuzzy method under various operating conditions. Results : The proposed DMZO‐PI+Fuzzy strategy performs significantly better than traditional approaches, according to simulation results. With a minimum tariff of 0.034 USD/kWh during off‐peak hours, charging costs can be lowered by up to 75.56%. On weekdays and weekends, average charging rates drop to 0.086 and 0.088 USD/kWh, respectively, representing cost savings of 45.26% and 56.11%. Also, under dynamic operating conditions, enhanced convergence speed and DC bus voltage stability are observed, and optimal renewable utilisation results in a maximum GHG emission reduction of 55.75%. Conclusion : The proposed DMZO‐PI+Fuzzy energy management framework offers an effective, reliable, and economical feasible EV charging solution for DC microgrids powered by renewable energy. The approach improves both economic and environmental performance by simultaneously optimising charging costs, the use of renewable resources, and efficient power management (PM) in DC MGs.