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Peer-reviewedCC BY 4.0Batteries

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

  • Xun Wang1
  • Xinwei Fan1
  1. 1School of Management Science and Engineering, China Jiliang University, Hangzhou 310018, China
Open at sourceDOICite

Abstract

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.

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Cite

BibTeXRIS
BibTeX
@article{wang2026normaldataonly,
  title = {{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}},
  author = {Xun Wang and Xinwei Fan},
  journal = {Batteries},
  year = {2026},
  month = oct,
  doi = {10.3390/batteries12110441},
  url = {https://www.mdpi.com/2313-0105/12/11/441},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - 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
AU  - Xun Wang
AU  - Xinwei Fan
PY  - 2026
DA  - 2026/10/22
JO  - Batteries
DO  - 10.3390/batteries12110441
UR  - https://www.mdpi.com/2313-0105/12/11/441
AB  - 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.
SN  - 2313-0105
ER  -