AI-Driven Dynamic Wireless EV Charging System: Battery Health Prediction and Energy Optimization Using IoT Data

Abstract: Charging is still the weakest part of owning an electric vehicle (EV). Sessions are long, public chargers are unevenly spread, and the battery tends to wear out sooner than owners expect. Most published work tackles only one of these problems: the wireless power link, the estimation of battery health, or the sensing of battery data. This paper describes the design of a dynamic wireless charging system that brings these pieces together and adds an energy- optimization step. Transmitter coils placed under the road supply power to a receiver coil on a moving vehicle by inductive coupling. An ESP32-based sensing unit records voltage, temperature, charge cycles and charging time and sends them to a cloud platform, where machine learning models are intended to estimate battery health and suggest changes to the power transfer. A web dashboard presents the results to the operator. The work reported here covers the first project stage: system architecture, component selection, preparation of a battery dataset and exploratory analysis. On a sample of ten records, battery health dropped from 95% to 87% between cycles 5 and 14 (r = -0.98), and higher temperature went with lower health (r = -0.63). The planned prediction models and the remaining hardware validation are also outlined.

Keywords: Dynamic wireless power transfer, electric vehicle, battery health prediction, Internet of Things, machine learning, energy optimization