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A novel OCV curve reconstruction and update method of lithium-ion batteries at different temperatures based on cloud data

Author

Listed:
  • Wang, Limei
  • Sun, Jingjing
  • Cai, Yingfeng
  • Lian, Yubo
  • Jin, Mengjie
  • Zhao, Xiuliang
  • Wang, Ruochen
  • Chen, Long
  • Chen, Jun

Abstract

Open-Circuit-Voltage (OCV) estimation is necessary for energy storage systems in electric vehicles (EVs) and energy storage systems (BESSs). The OCV-SOC curve is generally obtained by the low-rate current and the static methods. However, there is no long-term standing state of the battery during operation. This paper proposes a method to construct the complete OCV-SOC curve at different temperatures based on cloud data. Firstly, the OCV-SOC from the discharge segment is identified by the analogy method to verify the performance consistency of the battery under the operation condition and the laboratory. Secondly, the influence of temperature and ageing on the OCV-SOC curve is analyzed. Meanwhile, the adaptability of different OCV-SOC models is explored. An OCV-SOC model based on the improved electrode potential model suitable for different temperatures is then built. Thirdly, a method to construct a complete OCV-SOC curve from the charge segment is proposed based on the thermodynamic ideal material characteristics. The constructed OCV-SOC curve is also updated in real-time by the improved electrode potential model. Finally, the cloud data of different temperatures are used to verify the method. Results show that the method has high accuracy and reliability.

Suggested Citation

  • Wang, Limei & Sun, Jingjing & Cai, Yingfeng & Lian, Yubo & Jin, Mengjie & Zhao, Xiuliang & Wang, Ruochen & Chen, Long & Chen, Jun, 2023. "A novel OCV curve reconstruction and update method of lithium-ion batteries at different temperatures based on cloud data," Energy, Elsevier, vol. 268(C).
    • Handle: RePEc:eee:energy:v:268:y:2023:i:c:s0360544223001676
    DOI: 10.1016/j.energy.2023.126773
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    References listed on IDEAS

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    Cited by:

    1. Yu, Hanqing & Zhang, Lisheng & Wang, Wentao & Li, Shen & Chen, Siyan & Yang, Shichun & Li, Junfu & Liu, Xinhua, 2023. "State of charge estimation method by using a simplified electrochemical model in deep learning framework for lithium-ion batteries," Energy, Elsevier, vol. 278(C).
    2. Lian, Gaoqi & Ye, Min & Wang, Qiao & Li, Yan & Xia, Baozhou & Zhang, Jiale & Xu, Xinxin, 2024. "Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments," Energy, Elsevier, vol. 293(C).

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