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CSEE Journal of Power and Energy Systems 2022, 8(1): 258-270 https://doi.org/10.17775/CSEEJPES.2020.03260
Open Access | Issue | Published: 21 December 2020

On-board Diagnosis of Soft Short Circuit Fault in Lithium-ion Battery Packs for Electric Vehicles Using an Extended Kalman Filter

Show Author's Information Ruixin Yang,  Rui Xiong( ),  Weixiang Shen
National Engineering Laboratory for Electric Vehicles, Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Vic. 3122, Australia
Keywords:
fault diagnosis, electric vehicles, Battery safety, external short circuit, internal short circuit, soft short circuit
Cite this article:
Yang R, Xiong R, Shen W. On-board Diagnosis of Soft Short Circuit Fault in Lithium-ion Battery Packs for Electric Vehicles Using an Extended Kalman Filter. CSEE Journal of Power and Energy Systems, 2022, 8(1): 258-270. https://doi.org/10.17775/CSEEJPES.2020.03260
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Abstract Full text About this article

The safety of lithium-ion batteries in electric vehicles (EVs) is attracting more attention. To ensure battery safety, early detection is necessary of a soft short circuit (SC) which may evolve into severe SC faults, leading to fire or thermal runaway. This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter (EKF) for on-board applications in EVs. In the proposed method, the EKF is used to estimate the state of charge (SOC) of the faulty cell by adjusting a gain matrix based on real-time measured voltages. The SOC difference between the estimated SOC and the calculated SOC through coulomb counting for the faulty cell is employed to detect soft SC faults, and the soft SC resistance values are further identified to indicate the degree of fault severity. Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values. The experimental data are acquired to validate the proposed soft SC fault diagnosis method. The results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.


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About this article

On-board Diagnosis of Soft Short Circuit Fault in Lithium-ion Battery Packs for Electric Vehicles Using an Extended Kalman Filter

Show Author's information Ruixin Yang, Rui Xiong( ), Weixiang Shen
National Engineering Laboratory for Electric Vehicles, Department of Vehicle Engineering, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Vic. 3122, Australia

Abstract

The safety of lithium-ion batteries in electric vehicles (EVs) is attracting more attention. To ensure battery safety, early detection is necessary of a soft short circuit (SC) which may evolve into severe SC faults, leading to fire or thermal runaway. This paper proposes a soft SC fault diagnosis method based on the extended Kalman filter (EKF) for on-board applications in EVs. In the proposed method, the EKF is used to estimate the state of charge (SOC) of the faulty cell by adjusting a gain matrix based on real-time measured voltages. The SOC difference between the estimated SOC and the calculated SOC through coulomb counting for the faulty cell is employed to detect soft SC faults, and the soft SC resistance values are further identified to indicate the degree of fault severity. Soft SC experiments are developed to investigate the characteristics of a series-connected battery pack under different working conditions when one battery cell in the pack is short-circuited with different resistance values. The experimental data are acquired to validate the proposed soft SC fault diagnosis method. The results show that the proposed method is effective and robust in quickly detecting a soft SC fault and accurately estimating soft SC resistance.

Keywords: fault diagnosis, electric vehicles, Battery safety, external short circuit, internal short circuit, soft short circuit

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Publication history
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Publication history

Received: 11 July 2020
Revised: 07 October 2020
Accepted: 10 November 2020
Published: 21 December 2020
Issue date: January 2022

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© 2020 CSEE

Acknowledgements

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 51922006, 51877009).

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