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Intelligent Battery Management Systems for Electric Vehicles Using AI and Internet of Things Technologies
1 2 3 4 UG Scholar, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India. 5 Assistant Professor, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India.
Published Online: September-December 2025
Pages: 24-27
The rapid growth of electric vehicles (EVs) has increased the demand for advanced battery management systems capable of ensuring safety, reliability, efficiency, and extended battery lifespan. Batteries represent one of the most critical and expensive components of electric vehicles, making their effective monitoring and management essential for optimal vehicle performance. Conventional Battery Management Systems (BMS) primarily rely on predefined control algorithms and limited monitoring capabilities, which often struggle to address complex battery behaviors under varying operating conditions. Recent advancements in Artificial Intelligence (AI) and Internet of Things (IoT) technologies have created opportunities for developing intelligent battery management solutions that provide real-time monitoring, predictive analytics, and adaptive control. This study proposes an intelligent Battery Management System integrating AI algorithms and IoT-based communication technologies for electric vehicle applications. The framework utilizes smart sensors, cloud computing platforms, machine learning models, and wireless communication networks to continuously monitor battery parameters such as voltage, current, temperature, state of charge, and state of health. AI-driven predictive analytics enable accurate battery condition assessment, fault diagnosis, and remaining useful life estimation. IoT technologies facilitate real-time data transmission, remote monitoring, and centralized battery management. Performance evaluation demonstrates significant improvements in battery efficiency, charging optimization, fault detection accuracy, and battery lifespan prediction compared with traditional BMS approaches. The proposed framework supports safer and more efficient EV operation while reducing maintenance costs and improving energy utilization. The findings highlight the potential of AI and IoT technologies in enabling next-generation intelligent battery management systems for sustainable electric mobility.
