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Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group

Author

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  • Hua Zhang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
    College of Electronic Science, Northeast Petroleum University, Daqing 163318, China)

  • Lei Pei

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Jinlei Sun

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Kai Song

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Rengui Lu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Yongping Zhao

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Chunbo Zhu

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

  • Tiansi Wang

    (School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)

Abstract

In a parallel-connected battery group (PCBG), capacity degradation is usually caused by the inconsistency between a faulty cell and other normal cells, and the inconsistency occurs due to two potential causes: an aging inconsistency fault or a loose contacting fault. In this paper, a novel method is proposed to perform online and real-time capacity fault diagnosis for PCBGs. Firstly, based on the analysis of parameter variation characteristics of a PCBG with different fault causes, it is found that PCBG resistance can be taken as an indicator for both seeking the faulty PCBG and distinguishing the fault causes. On one hand, the faulty PCBG can be identified by comparing the PCBG resistance among PCBGs; on the other hand, two fault causes can be distinguished by comparing the variance of the PCBG resistances. Furthermore, for online applications, a novel recursive-least-squares algorithm with restricted memory and constraint (RLSRMC), in which the constraint is added to eliminate the “imaginary number” phenomena of parameters, is developed and used in PCBG resistance identification. Lastly, fault simulation and validation results demonstrate that the proposed methods have good accuracy and reliability.

Suggested Citation

  • Hua Zhang & Lei Pei & Jinlei Sun & Kai Song & Rengui Lu & Yongping Zhao & Chunbo Zhu & Tiansi Wang, 2016. "Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group," Energies, MDPI, vol. 9(5), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:5:p:387-:d:70558
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    References listed on IDEAS

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    Cited by:

    1. Changwen Zheng & Yunlong Ge & Ziqiang Chen & Deyang Huang & Jian Liu & Shiyao Zhou, 2017. "Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter," Energies, MDPI, vol. 10(11), pages 1-14, November.
    2. 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.
    3. Zhao, Yang & Liu, Peng & Wang, Zhenpo & Zhang, Lei & Hong, Jichao, 2017. "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Elsevier, vol. 207(C), pages 354-362.
    4. Ivana Semanjski & Sidharta Gautama, 2016. "Forecasting the State of Health of Electric Vehicle Batteries to Evaluate the Viability of Car Sharing Practices," Energies, MDPI, vol. 9(12), pages 1-17, December.

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