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A Time-Efficient and Accurate Open Circuit Voltage Estimation Method for Lithium-Ion Batteries

Author

Listed:
  • Yingjie Chen

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Geng Yang

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Xu Liu

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Zhichao He

    (Department of Automation, Tsinghua University, Beijing 100084, China)

Abstract

The open circuit voltage (OCV) of lithium-ion batteries is widely used in battery modeling, state estimation, and management. However, OCV is a function of state of charge (SOC) and battery temperature ( T bat ) and is very hard to estimate in terms of time efficiency and accuracy. This is because two problems arise in normal operations: (1) T bat changes with the current ( I ), which makes it very hard to obtain the data required to estimate OCV—terminal voltage ( U ) data of different I under the same T bat ; (2) the difference between U and OCV is a complex nonlinear function of I and is very difficult to accurately calculate. Therefore, existing methods have to design special experiments to avoid these problems, which are very time consuming. The proposed method consists of a designed test and a data processing algorithm. The test is mainly constant current tests (CCTs) of large I , which is time-efficient in obtaining data. The algorithm solves the two problems and estimates OCV accurately using the test data. Experimental results and analyses showed that experimental time was reduced and estimation accuracy was adequate.

Suggested Citation

  • Yingjie Chen & Geng Yang & Xu Liu & Zhichao He, 2019. "A Time-Efficient and Accurate Open Circuit Voltage Estimation Method for Lithium-Ion Batteries," Energies, MDPI, vol. 12(9), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1803-:d:230450
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    References listed on IDEAS

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    1. Zhichao He & Geng Yang & Languang Lu, 2016. "A Parameter Identification Method for Dynamics of Lithium Iron Phosphate Batteries Based on Step-Change Current Curves and Constant Current Curves," Energies, MDPI, vol. 9(6), pages 1-24, June.
    2. Waag, Wladislaw & Käbitz, Stefan & Sauer, Dirk Uwe, 2013. "Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application," Applied Energy, Elsevier, vol. 102(C), pages 885-897.
    3. Jie Yang & Chunyu Du & Ting Wang & Yunzhi Gao & Xinqun Cheng & Pengjian Zuo & Yulin Ma & Jiajun Wang & Geping Yin & Jingying Xie & Bo Lei, 2018. "Rapid Prediction of the Open-Circuit-Voltage of Lithium Ion Batteries Based on an Effective Voltage Relaxation Model," Energies, MDPI, vol. 11(12), pages 1-15, December.
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    Cited by:

    1. Miaomiao Zeng & Peng Zhang & Yang Yang & Changjun Xie & Ying Shi, 2019. "SOC and SOH Joint Estimation of the Power Batteries Based on Fuzzy Unscented Kalman Filtering Algorithm," Energies, MDPI, vol. 12(16), pages 1-15, August.
    2. Hou, Jiayang & Xu, Jun & Lin, Chuanping & Jiang, Delong & Mei, Xuesong, 2024. "State of charge estimation for lithium-ion batteries based on battery model and data-driven fusion method," Energy, Elsevier, vol. 290(C).

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