A novel unsupervised anomaly detection and traceability method f 2027 Applie

Voltage inconsistencies in lithium-ion battery packs are a primary driver of performance degradation and thermal runaway. Accurate diagnosis from real-world operational data remains hindered by highly dynamic driving conditions, continuous measurement noise, and scarce labeled faults. To address these challenges, an unsupervised anomaly detection framework utilizing a time-segmented Density-Based Spatial Clustering of Applications with Noise algorithm is proposed. High-dimensional voltage features, capturing both transient fluctuations and long-term aging behaviors, are extracted via a multi-scale sliding window mechanism. Principal Component Analysis is subsequently applied to decouple absolute voltage levels from dynamic inconsistency patterns. Time-segmented clustering then facilitates highly efficient local anomaly detection, generating a continuous Consistency Health Index for the quantitative grading of individual cell health. The framework was rigorously validated using actual data.