K. Reddy

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

Simulation results 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.