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State-of-charge estimation and uncertainty for lithium-ion battery strings

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  • Truchot, Cyril
  • Dubarry, Matthieu
  • Liaw, Bor Yann

Abstract

The state-of-charge (SOC) estimation is of extreme importance for the reliability and safety of battery operation. How to estimate SOC and, to some degree, the SOC convention itself, is still a subject of great interest. Here a viable SOC convention valid for single cells and multi-cell strings is proposed and validated. Using a 3S1P string as an illustration in this work, the direct inference from a correct open circuit voltage versus SOC (OCV=f(SOC)) correspondence based on the proposed SOC convention is the best method for accurate SOC estimation among several possible approaches for strings. The thermodynamic aspect on this SOC convention is explained. Uncertainties in actual applications are also discussed. The understanding on this accurate SOC estimation approach shall facilitate reliable battery control and management.

Suggested Citation

  • Truchot, Cyril & Dubarry, Matthieu & Liaw, Bor Yann, 2014. "State-of-charge estimation and uncertainty for lithium-ion battery strings," Applied Energy, Elsevier, vol. 119(C), pages 218-227.
    • Handle: RePEc:eee:appene:v:119:y:2014:i:c:p:218-227
    DOI: 10.1016/j.apenergy.2013.12.046
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    12. Lei Pei & Cheng Yu & Tiansi Wang & Jiawei Yang & Wanlin Wang, 2024. "A Training-Free Estimation Method for the State of Charge and State of Health of Series Battery Packs under Various Load Profiles," Energies, MDPI, vol. 17(8), pages 1-20, April.
    13. Lin, Cheng & Yu, Quanqing & Xiong, Rui & Wang, Le Yi, 2017. "A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries," Applied Energy, Elsevier, vol. 205(C), pages 892-902.
    14. Li, Zhirun & Xiong, Rui & Mu, Hao & He, Hongwen & Wang, Chun, 2017. "A novel parameter and state-of-charge determining method of lithium-ion battery for electric vehicles," Applied Energy, Elsevier, vol. 207(C), pages 363-371.
    15. Xia, L. & Najafi, E. & Li, Z. & Bergveld, H.J. & Donkers, M.C.F., 2017. "A computationally efficient implementation of a full and reduced-order electrochemistry-based model for Li-ion batteries," Applied Energy, Elsevier, vol. 208(C), pages 1285-1296.
    16. Lyons, P.F. & Wade, N.S. & Jiang, T. & Taylor, P.C. & Hashiesh, F. & Michel, M. & Miller, D., 2015. "Design and analysis of electrical energy storage demonstration projects on UK distribution networks," Applied Energy, Elsevier, vol. 137(C), pages 677-691.
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    18. He, Hongwen & Xiong, Rui & Peng, Jiankun, 2016. "Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform," Applied Energy, Elsevier, vol. 162(C), pages 1410-1418.
    19. Zheng, Yuejiu & Ouyang, Minggao & Li, Xiangjun & Lu, Languang & Li, Jianqiu & Zhou, Long & Zhang, Zhendong, 2016. "Recording frequency optimization for massive battery data storage in battery management systems," Applied Energy, Elsevier, vol. 183(C), pages 380-389.
    20. Thomas Bruen & James Michael Hooper & James Marco & Miguel Gama & Gael Henri Chouchelamane, 2016. "Analysis of a Battery Management System (BMS) Control Strategy for Vibration Aged Nickel Manganese Cobalt Oxide (NMC) Lithium-Ion 18650 Battery Cells," Energies, MDPI, vol. 9(4), pages 1-20, April.
    21. Ghulam E Mustafa Abro & Saiful Azrin B. M. Zulkifli & Kundan Kumar & Najib El Ouanjli & Vijanth Sagayan Asirvadam & Mahmoud A. Mossa, 2023. "Comprehensive Review of Recent Advancements in Battery Technology, Propulsion, Power Interfaces, and Vehicle Network Systems for Intelligent Autonomous and Connected Electric Vehicles," Energies, MDPI, vol. 16(6), pages 1-31, March.

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