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

Zhenyu Sun, Zhenpo Wang, Peng Liu, Zian Qin*, Yong Chen*, Yang Han, Peng Wang, Pavol Bauer

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

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 lithium-ion batteries is proposed. By applying both the discrete Frchet 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.
Original languageEnglish
Article number9770404
Number of pages11
JournalIEEE Transactions on Power Electronics
DOIs
Publication statusE-pub ahead of print - 2022

Keywords

  • Lithium-ion battery
  • Local outlier factor
  • Discrete Fre chet distance
  • Fault diagnosis

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