Wireless EV Charging System with BMS & ML-Based Battery Life Prediction

Author(s)	Ms. Rashmi Dhool, Mr. Kiran P V
Country	India
Abstract	Wireless charging for electric vehicles (EVs) is an innovative solution that supports the vision of smart cities by enabling seamless and automated energy transfer. The proposed system removes the need for physical plugs and cables by using resonant inductive coupling for wireless power transfer. The system integrates ESP32 microcontrollers, IoT communication, real-time monitoring, and machine learning-based battery health prediction. Charging infrastructure can be embedded in parking spaces, garages, roads, and transit hubs. When an EV parks over a charging pad, energy transfer begins automatically without driver intervention. The proposed system enhances convenience, safety, reliability, and battery lifespan while supporting future smart city infrastructure. Machine learning algorithms analyze voltage, current, charging cycles, and operational data to estimate battery State of Health (SoH) and Remaining Useful Life (RUL). IoT integration enables cloud-based monitoring, analytics, and predictive maintenance. The system provides a scalable and intelligent charging solution suitable for sustainable transportation ecosystems.
Keywords	Wireless Charging, Electric Vehicles, ESP32, IoT, Smart Cities, Machine Learning, Battery Health Prediction, Resonant Inductive Coupling
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-18

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