Real-Time Embedded Intelligent Control of Hybrid Renewable Energy Systems for EV Charging

In response to the challenges of electric mobility in off-grid contexts, this study introduces a novel and pragmatic solution: an intelligent, embedded EV charging system capable of anticipating energy availability using external weather forecasts. An embedded Model Predictive Control (MPC) scheme was implemented on an ESP32 microcontroller, incorporating real-time solar and wind forecasts transmitted via LoRa. Unlike conventional approaches that are often centralized or resource-intensive, the proposed architecture enables localized, forecast-aware decision making, while respecting physical constraints (SOC, power limits, system stability) within the limits of embedded hardware. The proposed system was fully validated through functional simulations (data acquisition, processing, display, and physical actuation). Results confirm the feasibility of real-time, stable, and proactive energy management, laying the foundation for smart, resilient, and autonomous renewable-based EV charging stations tailored to remote areas and decentralized microgrids.