Multi-dimension Fault Diagnosis of Battery System in Electric Vehicles Based on Real-world Thermal Runaway Vehicle Data

Guozhen Zhang, Da Li, Peng Liu, Zhaosheng Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Battery system fault diagnosis and thermal runaway warning are critical to ensure the safe operation of electric vehicles. In this paper, the multi-dimensional fault diagnosis method is used to quantitatively analyze the whole-life-cycle data of real-world thermal runaway vehicle. Firstly, the battery voltage range diagnosis, outlier unit identification, voltage jump diagnosis and temperature range diagnosis are carried out to mine the potential fault of the power battery. Then the cell voltage and probe temperature diagnosis of the charge and discharge segment before thermal runaway are performed to accurately locate thermal runaway potential cells and provide early warning of thermal runaway. The conclusion of the fault diagnosis is verified by battery disassembling analysis. The results show that the potential failure of the cell No. 69 is obvious. During the last charging segment before thermal runaway, a large voltage drop occurred to the cell No. 69, and the temperature of temperature probe near it in the same module rose too fast. This method can accurately locate and warn potential thermal runaway cells one day before thermal runaway.

Original languageEnglish
Title of host publicationiSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference
Subtitle of host publicationGrid Modernization for Energy Revolution, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2830-2835
Number of pages6
ISBN (Electronic)9781728149301
DOIs
Publication statusPublished - Nov 2019
Event2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019 - Beijing, China
Duration: 21 Nov 201923 Nov 2019

Publication series

NameiSPEC 2019 - 2019 IEEE Sustainable Power and Energy Conference: Grid Modernization for Energy Revolution, Proceedings

Conference

Conference2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019
Country/TerritoryChina
CityBeijing
Period21/11/1923/11/19

Keywords

  • big data
  • fault diagnosis
  • lithium-ion battery
  • thermal runaway

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