Machine learning-enabled energy management strategies for hybrid renewable-powered ultra-fast charging infrastructure

ABSTRACT Ultra-fast electric vehicle (EV) charging stations operating at power levels above 350 kW introduce critical challenges related to grid peak demand, high operating cost, renewable intermittency, and battery stress. This paper presents a machine learning-enabled energy management system for a hybrid renewable-powered ultra-fast charging station integrating photovoltaic generation, battery energy storage system, dispatchable auxiliary sources, and grid supply. The proposed EMS operates at a supervisory level and coordinates energy flows under stochastic EV charging demand, time-varying electricity tariffs (₹4–₹10/kWh), and uncertain renewable generation. A learning-based decision framework is developed using a reinforcement learning policy trained over 150 episodes, incorporating renewable and EV demand forecasts with ±10% uncertainty. The EMS performs multi-objective optimization by minimizing grid energy cost and peak power demand while achieving a balanced trade-off between renewable energy utilization, grid stability, and economic performance, and maintaining battery state-of-charge within safe operating limits (0.2–0.9). Simulation results over a 24-hour operating horizon demonstrate that the proposed ML-EMS achieves a 20–35% reduction in total grid energy cost, 25–40% peak grid power reduction, and achieves a balanced trade-off between renewable utilization, grid stability, and economic performance compared to a conventional rule-based EMS. The results validate the effectiveness of machine learning-driven energy management for reliable, grid-friendly, and cost-efficient operation of next- generation ultra-fast EV charging infrastructure.