Normal-Data-Only Fault Detection and Faulty-Cell Localization in Heterogeneous Electric-Vehicle Battery Packs Using a Hotspot-Suppressed and Alarm-Consistent Dual-Branch Attention Transformer

This paper presents HAC-DBAT, a normal-data-only dual-branch Transformer framework for record-level fault recognition and faulty-cell localization in heterogeneous electric-vehicle battery packs. The dataset comprises 88 vehicle records, including 34 normal training records and a held-out test partition of 50 records containing seven labeled faults and 13 labeled faulty cells. On the test partition, the record-level F1-score is 0.9333, the conditional faulty-cell F1-score is 0.8889, and the end-to-end faulty-cell F1-score is 0.8276. The framework integrates validity-masked pack alignment, residual-conditioned dual-branch reconstruction, normal-reference thresholding and hotspot-aware cell ranking. Its evaluation concerns fault-record identification and faulty-cell localization, rather than verified thermal-runaway lead time.