AI-Powered Battery Charging System Using Machine Learning Algorithms for EVs

This paper presents an intelligent charging system powered by AI to boost performance and improve the State of Charge (SoC) and State of Health (SoH) of batteries in Electric Vehicles (EVs). Despite advancements in design and electrical specifications, the significance of effectively charging EV batteries is essential for ensuring safety, efficiency, and longevity. The prototype features a Raspberry Pi as its main controller, along with sensors that monitor key battery metrics including voltage, current, temperature, and pressure. The proposed system employs machine learning algorithms to forecast critical battery metrics such as SoC, SoH, and charging capacity based on the collected sensor data. These metrics are gathered in real-time, allowing the implementation of adaptive charging strategies that maintain consistent battery performance while adjusting to real-time variations in battery conditions to avoid issues like overheating, overcharging, and deterioration. This method not only prolongs battery life but also minimizes safety hazards. The proposed system is scalable, cost-efficient, and superior to traditional charging systems. Furthermore, it can be paired with renewable energy sources to further improve energy efficiency. This paper aims to promote the creation of an environmentally sustainable and smart battery charging system for electric vehicles