Detection of voltage fault in the battery system of electric vehicles using statistical analysis

Journal article (2021)

Authors

S.Z. Sun Electrical Sustainable Energy , DC systems, Energy conversion & Storage - EEMCS , Beijing Institute of Technology
Yang Han The University of Manchester
Zhenpo Wang Beijing Institute of Technology, Beijing Institute of Technology Chongqing Innovation Center, Chongqing
Yong Chen Beijing Information Science and Technology University, Beijing
Peng Liu Beijing Institute of Technology, Beijing Institute of Technology Chongqing Innovation Center, Chongqing
Z. Qin Electrical Sustainable Energy , DC systems, Energy conversion & Storage - EEMCS
Zhaosheng Zhang Beijing Institute of Technology
Zhiqiang Wu Beijing Institute of Technology
Chunbao Song Beijing Institute of Technology
Research Group
DC systems, Energy conversion & Storage (EEMCS) (TU Delft)
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Published Date

2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Electrical Sustainable Energy

Research Group

DC systems, Energy conversion & Storage

Abstract

It is vital to detect the safety state and identify faults of the battery pack for the safe operation of electric vehicles. The voltage faults such as over-voltage and under-voltage imply more serious battery faults including short-circuit and thermal runaway. The voltage abnormal fluctuation is a warning signal of short-circuit, over-voltage and under-voltage. This paper proposes a scheme of three-layer fault detection method for lithium-ion batteries based on statistical analysis. The first layer fault detection is based on the thresholds of over-charge and over-discharge of a battery pack. In the second layer, confidence interval estimation is applied to identify risky cells. In the third layer, correlation and variability of all cells in one battery pack are analyzed by using an improved K-means method to identify abnormal voltage fluctuation over a certain period. The validity and feasibility of the proposed method are verified by real vehicle data from the National Big Data Alliance of New Energy Vehicles.

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