Data-Driven Fault Diagnosis of Lithium-Ion Battery Overdischarge in Electric Vehicles

Naifeng Gan, Zhenyu Sun, Zhaosheng Zhang, Shiqi Xu, Peng Liu*, Zian Qin*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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The overdischarge can significantly degrade a lithium-ion (Li-ion) battery's lifetime. Therefore, it is important to detect the overdischarge and prevent severe damage of the Li-ion battery. Depending on the battery technology, there is a minimum voltage (cutoff voltage) that the battery is allowed to be discharged in common practice. Once the battery voltage is below the cutoff voltage, it is considered as overdischarge. However, overdischarge will not lead to immediate failure of the battery, and if it is not detected, the battery voltage can increase above the cutoff voltage during charging process. How to detect an overdischarge has happened, while the current voltage is larger than the cutoff voltage, thus becomes very challenging. In this article, a machine learning based two-layer overdischarge fault diagnosis strategy for Li-ion batteries in electric vehicles is proposed. The first layer is to detect the overdischarge by comparing the battery voltage with cutoff voltage, like what is utilized in common practice. If the battery voltage is larger than the cutoff voltage, the second layer, which is a detection approach based on eXtreme Gradient Boosting algorithm, is triggered. The second layer is employed to detect the previous overdischarge. The proposed method is validated by real electric vehicle data.
Original languageEnglish
Article number9583917
Pages (from-to)4575-4588
Number of pages14
JournalIEEE Transactions on Power Electronics
Issue number4
Publication statusPublished - 2022


  • Electric vehicle (EVS)
  • extreme gradient boosting (XGboost)
  • fault diagnosis
  • lithium-ion battery (LIB)
  • overdischarge


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