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The improved open-circuit voltage characterization test using active polarization voltage reduction method

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  • Yang, Jufeng
  • Huang, Wenxin
  • Xia, Bing
  • Mi, Chris

Abstract

The correlation between state of charge (SoC) and open-circuit voltage (OCV) is a crucial characteristic parameter in many aspects of the battery management system (BMS). However, it is a challenging task to obtain the accurate SoC-OCV correlation with a high test efficiency. In this paper, an improved OCV characterization test is proposed to actively reduce the polarization voltage. Based on the third-order equivalent circuit model (ECM), two sets of current pulses are applied to accelerate the convergence of the battery terminal voltage, thus the test time is effectively shortened compared to the conventional incremental OCV characterization test. Furthermore, the parametric sensitivity of the imposed current excitation to battery model parameters is analyzed. Subsequently, the parametric determination method for the imposed current excitation is provided. Experiments are conducted on a lithium-ion polymer battery to prove the feasibility of the proposed test procedure. Comparison with the conventional OCV characterization test further demonstrated the superiority of the proposed test procedure.

Suggested Citation

  • Yang, Jufeng & Huang, Wenxin & Xia, Bing & Mi, Chris, 2019. "The improved open-circuit voltage characterization test using active polarization voltage reduction method," Applied Energy, Elsevier, vol. 237(C), pages 682-694.
    • Handle: RePEc:eee:appene:v:237:y:2019:i:c:p:682-694
    DOI: 10.1016/j.apenergy.2019.01.060
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    Cited by:

    1. Yang, Jufeng & Cai, Yingfeng & Pan, Chaofeng & Mi, Chris, 2019. "A novel resistor-inductor network-based equivalent circuit model of lithium-ion batteries under constant-voltage charging condition," Applied Energy, Elsevier, vol. 254(C).
    2. Gao, Yizhao & Sun, Ziqiang & Zhang, Dong & Shi, Dapai & Zhang, Xi, 2023. "Determination of half-cell open-circuit potential curve of silicon-graphite in a physics-based model for lithium-ion batteries," Applied Energy, Elsevier, vol. 349(C).
    3. Ko, Chi-Jyun & Chen, Kuo-Ching, 2024. "Using tens of seconds of relaxation voltage to estimate open circuit voltage and state of health of lithium ion batteries," Applied Energy, Elsevier, vol. 357(C).
    4. Chen, Yingjie & Zhang, Huaqin & Hong, Jichao & Hou, Yankai & Yang, Jingsong & Zhang, Chi & Ma, Shikun & Zhang, Xinyang & Yang, Haixu & Liang, Fengwei & Li, Kerui, 2024. "Lithium plating detection of lithium-ion batteries based on the improved variance entropy algorithm," Energy, Elsevier, vol. 299(C).
    5. Ahmed, Mostafa Shaban & Raihan, Sheikh Arif & Balasingam, Balakumar, 2020. "A scaling approach for improved state of charge representation in rechargeable batteries," Applied Energy, Elsevier, vol. 267(C).
    6. Yao, Jiachi & Chang, Zhonghao & Han, Te & Tian, Jingpeng, 2024. "Semi-supervised adversarial deep learning for capacity estimation of battery energy storage systems," Energy, Elsevier, vol. 294(C).

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