Edge-intelligent electric vehicle charging coordination for grid load balancing and renewable integration

Abstract

The rapid growth of electric vehicles (EVs) creates challenges for grid stability, peak demand management, and renewable energy integration. Conventional cloud-centric charging coordination systems rely on continuous communication and suffer from latency that limits real-time responsiveness. This study introduces a novel distributed edge-intelligent EV charging coordination framework that integrates behavioral prediction, grid-aware scheduling, renewable-aware optimization, and localized AI inference within a communication-efficient IoT architecture. Lightweight models (CNN+LSTM, XGBoost, and Random Forest) are deployed on Jetson Orin Nano and Raspberry Pi 5 devices to enable low-latency decision-making with reduced reliance on the cloud. The framework is validated using a large-scale U.S. Department of Energy dataset. Experimental results demonstrate 27.6% improvement in station utilization, 24.5% reduction in peak grid load, and 29.8% decrease in user charging costs, with predictive performance reaching R² = 0.92. The findings provide a scalable and communication-efficient reference design for data-driven EV charging coordination in smart grid systems.