Lithium-Ion Battery Fault Early-Warning Method Based on DTW-DBSCAN

In light of the pervasive utilization of lithium-ion batteries across diverse domains, concerns regarding their safety and reliability have gained significant prominence. We put forward a research methodology for the development of an early warning system for lithium-ion batteries, underpinned by the principles of Dynamic Time Warping (DTW) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). DTW effectively addresses the issue of inconsistent time scales caused by charging and discharging processes, environmental factors, etc., and can accurately capture subtle changes in the operating state of lithium-ion batteries. DBSCAN, based on the density of data points, flexibly identifies different pattern clusters in lithium-ion battery operation data, thus distinguishing between normal and abnormal operating states of different levels. The research proposed in this paper provides novel concepts and methodologies for the safety monitoring and early warning of lithium-ion batteries, thus offering a foundation for the preventive maintenance of battery safety.