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Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method

Author

Listed:
  • Minhwan Seo

    (Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea)

  • Taedong Goh

    (Department of Creative IT Excellence Engineering and Future IT Innovation Laboratory, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea)

  • Minjun Park

    (Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea)

  • Gyogwon Koo

    (Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea)

  • Sang Woo Kim

    (Department of Electrical Engineering, Pohang University of Science and Technology, 77 Cheongam-Ro, Nam-Gu, Pohang 37673, Korea)

Abstract

Early detection of an internal short circuit (ISCr) in a Li-ion battery can prevent it from undergoing thermal runaway, and thereby ensure battery safety. In this paper, a model-based switching model method (SMM) is proposed to detect the ISCr in the Li-ion battery. The SMM updates the model of the Li-ion battery with ISCr to improve the accuracy of ISCr resistance R I S C f estimates. The open circuit voltage (OCV) and the state of charge (SOC) are estimated by applying the equivalent circuit model, and by using the recursive least squares algorithm and the relation between OCV and SOC. As a fault index, the R I S C f is estimated from the estimated OCVs and SOCs to detect the ISCr, and used to update the model; this process yields accurate estimates of OCV and R I S C f . Then the next R I S C f is estimated and used to update the model iteratively. Simulation data from a MATLAB/Simulink model and experimental data verify that this algorithm shows high accuracy of R I S C f estimates to detect the ISCr, thereby helping the battery management system to fulfill early detection of the ISCr.

Suggested Citation

  • Minhwan Seo & Taedong Goh & Minjun Park & Gyogwon Koo & Sang Woo Kim, 2017. "Detection of Internal Short Circuit in Lithium Ion Battery Using Model-Based Switching Model Method," Energies, MDPI, vol. 10(1), pages 1-13, January.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:1:p:76-:d:87340
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Da Li & Zhaosheng Zhang & Peng Liu & Zhenpo Wang, 2019. "DBSCAN-Based Thermal Runaway Diagnosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 12(15), pages 1-15, August.
    2. Minhwan Seo & Taedong Goh & Minjun Park & Sang Woo Kim, 2018. "Detection Method for Soft Internal Short Circuit in Lithium-Ion Battery Pack by Extracting Open Circuit Voltage of Faulted Cell," Energies, MDPI, vol. 11(7), pages 1-18, June.
    3. Jichao Hong & Zhenpo Wang & Peng Liu, 2017. "Big-Data-Based Thermal Runaway Prognosis of Battery Systems for Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-16, July.
    4. Zhang, Guangxu & Wei, Xuezhe & Tang, Xuan & Zhu, Jiangong & Chen, Siqi & Dai, Haifeng, 2021. "Internal short circuit mechanisms, experimental approaches and detection methods of lithium-ion batteries for electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    5. Yang, Qifan & Sun, Jinlei & Kang, Yongzhe & Ma, Hongzhong & Duan, Dawei, 2023. "Internal short circuit detection and evaluation in battery packs based on transformation matrix and an improved state-space model," Energy, Elsevier, vol. 276(C).
    6. Ma, Mina & Wang, Yu & Duan, Qiangling & Wu, Tangqin & Sun, Jinhua & Wang, Qingsong, 2018. "Fault detection of the connection of lithium-ion power batteries in series for electric vehicles based on statistical analysis," Energy, Elsevier, vol. 164(C), pages 745-756.
    7. Jian Yang & Jaewook Jung & Samira Ghorbanpour & Sekyung Han, 2022. "Data–Driven Fault Diagnosis and Cause Analysis of Battery Pack with Real Data," Energies, MDPI, vol. 15(5), pages 1-19, February.
    8. Bosong Zou & Lisheng Zhang & Xiaoqing Xue & Rui Tan & Pengchang Jiang & Bin Ma & Zehua Song & Wei Hua, 2023. "A Review on the Fault and Defect Diagnosis of Lithium-Ion Battery for Electric Vehicles," Energies, MDPI, vol. 16(14), pages 1-19, July.

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