International Journal of Scientific Research and Engineering Development

Battery packs are the most critical components of Electric Vehicles (EVs), directly affecting vehicle performance, driving range, safety, and lifespan. Cell imbalance caused by manufacturing variations, aging effects, and temperature differences can significantly reduce battery efficiency and reliability. This paper presents an Artificial Intelligence (AI) and Machine Learning (ML) based Battery Monitoring and Cell Balancing System for Electric Vehicles. The proposed system continuously monitors battery voltage, current, and temperature through IoT-enabled sensors and employs machine learning techniques to identify cell imbalance conditions in real time. The developed system utilizes ESP32-based hardware architecture integrated with cloud connectivity for remote monitoring and predictive analysis. AI-based algorithms are used to estimate battery parameters, detect abnormal conditions, and initiate balancing operations to maintain uniform charge distribution among cells. Experimental results demonstrate effective battery monitoring, improved balancing accuracy, reduced energy loss, and enhanced battery life. The proposed solution provides a scalable and intelligent framework for next-generation EV battery management systems.

Anushka Chavan, Shivani Kale, Varun Wagh, Sonali Sonwane, Swaraj Dhumal, Dr.Hafiz Shaikh,"EV Battery Monitoring and Cell Balancing Using AI and Machine Learning" International Journal of Scientific Research and Engineering Development, V9(3): Page(1976-1982) May-June 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.