Data Driven Techniques for Fault Detection in Lithium Ion Battery

In the modern world, the widespread adoption of Lithium-ion batteries (LIB) presents challenges due to their vulnerability to faults, which can lead to serious incidents such as fires or explosions. Electric vehicles (EV), in particular, experience a high frequency of battery-related accidents, underscoring the urgent need for effective monitoring and maintenance of battery health. Our initiative proposes a proactive strategy utilizing machine learning (ML) algorithms to accurately detect and diagnose faults. By analyzing extensive datasets based on battery performance characteristics like voltage, current, and temperature, our goal is to elevate fault diagnosis capabilities and ensure safety and dependability. Unlike conventional diagnostic methods that may lack scalability or precision, ML-based data-driven approaches leverage both historical and real-time data to enhance accuracy. In this regard, various ML algorithms have been incorporated to identify the different battery faults. This paper advances a data-driven approach to fault detection in LIBs using ML techniques, such as Random Forest (RF), K-Nearest Neighbour (KNN), Logistic Regression, and Support Vector Machines (SVM), whose primary intent is the enhancement of safety and reliability in EVs. In terms of accuracy and precision random forest and KNN algorithms outperforms well in comparison to other well know techniques.