|Intelligent Battery Management System with AI and IoT for Enhanced EV Efficiency

The growing demand for electric vehicles (EVs) has created the need for a sophisticated Battery Management System (BMS) to maximize battery performance, safety, and life. This paper proposes an IoT-based BMS with Machine Learning (ML) and Artificial Intelligence (AI) for continous monitoring and predictive maintenance of EV batteries. The system continuously monitors critical battery metrics such as State of Charge (SOC), State of Health (SOH), voltage, current, and temperature and AI-powered algorithms analyze historyoriented and real-time data to foretell near-future battery failure, extend charging cycles, and maximize total energy efficiency. For improving operating safety, the system incorporates automatic charging terminations, short-circuit protection, and sensor fault detection, lowering risks related to battery failure. In addition, a web and mobile-based interface offers remote access, displaying real-time information, notifications, and battery diagnostics for effective management. The suggested BMS aims to reduce downtime, prolong battery life, and maximize energy consumption, allowing for early fault detection before problems become severe. By using predictive analytics and IoT-based automation, this system greatly improves EV battery reliability, efficiency, and sustainability, making it an integral part in the evolution of smart electric mobility solutions.