An online data driven fault diagnosis and thermal runaway early warning for electric vehicle batteries

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Zhenyu Sun
  • Zhenpo Wang
  • Peng Liu
  • Zian Qin
  • Yong Chen
  • Peng Wang
  • Pavol Baurer


Battery fault diagnosis is crucial for stable, reliable, and safe operation of electric vehicles, especially the thermal runaway early warning. Developing methods for early failure detection and reducing safety risks from failing high energy lithium-ion batteries has become a major challenge for industry. In this work, a real-time early fault diagnosis scheme for lithiumion batteries is proposed. By applying both the discrete Fréchet distance (DFD) and local outlier factor (LOF) to the voltage and temperature data of the battery cell/module that measured in real time, the battery cell that will have thermal runaway is detected before thermal runaway happens. Compared with the widely used single parameter based diagnosis approach, the proposed one considerably improve the reliability of the fault diagnosis and reduce the false diagnosis rate. The effectiveness of the proposed method is validated with the operational data from electric vehicles with/without thermal runaway in daily use.

Bibliographical metadata

Original languageEnglish
JournalIEEE Transactions on Power Electronics
Publication statusAccepted/In press - 28 Apr 2022