International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Wireless roadway charging is rapidly becoming a promising alternative to traditional plug-in and stationary EV charging techniques, which frequently suffer from long wait times and limited convenience. In this project, we present a solar-powered dynamic charging system capable of supplying energy to electric cars when they are moving. The system uses photovoltaic panels placed along or integrated into the roadway to harvest solar energy, which is then used to energize inductive transmitter coils embedded beneath the road surface. As an EV drives over these coils, its onboard receiver coil captures the transmitted energy, enabling continuous, interruption-free charging and reducing concerns related to battery range. Since solar energy output varies with weather conditions, the system incorporates a Long Short-Term Memory (LSTM) deep learning model to accurately forecast factors such as solar irradiance, temperature, and cloud cover. These forecasts aid in estimating power availability in real time and guarantee a steady and dependable charging process. The method is appropriate for future smart and sustainable transportation networks because experimental testing shows stable wireless power transfer, precise weather forecasting with a mean absolute error of about 24, and effective integration of all system modules.

Keywords: Dynamic Wireless Charging, Solar Energy, Electric Vehicles, LSTM Weather Forecasting, Inductive Power Transfer, Embedded Systems

Downloads: | DOI: 10.17148/IJARCCE.2025.1412139