Lithium-ion batteries play a crucial role in electric vehicles, renewable energy systems, and portable electronics because of their high energy density and long cycle life. However, challenges like overcharging, overheating, internal short circuits, and gradual degradation can lower performance, reduce lifespan, and create safety risks. The early fault detection through continuous monitoring is essential to address these safety concerns, This study presents a framework that uses sensor measurements such as voltage, current, temperature, and state of charge to assess the health of lithium batteries. The method combines an unsupervised learning algorithm, Isolation Forest, with a heuristic fault analysis approach to spot unusual behavior and classify the states as normal or faulty. Experimental investigations identified fifteen different fault occurrences, including internal short circuits and thermal issues. Correlation assessments show that the framework effectively detects sudden faults. The system enhances safety and reliability by identifying faults early. With an accuracy of about 90 to 95%, it demonstrates significant potential for real-time battery health monitoring.
Arulmozhi et al. (Tue,) studied this question.