Comparative study of anomaly detection techniques for monitoring Lithium Iron Phosphate – LiFePO4 batteries

This research analyzes and compares the application of different intelligent supervised classification techniques for detecting anomalies in power cells. For this purpose, a labeled dataset is obtained and generated in which samples of the different charge and discharge cycles of a Lithium Iron Phosphate - LiFePO4 (LFP) battery commonly used in electric vehicles are collected. The final classifiers present successful results.