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A study on the impact of open circuit voltage tests on state of charge estimation for lithium-ion batteries

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  • Lin, Cheng
  • Yu, Quanqing
  • Xiong, Rui
  • Wang, Le Yi

Abstract

The open circuit voltage (OCV) is of essential importance for accurate estimation of the state of charge (SoC) in lithium-ion battery (LiB). The OCV-SoC relationship is typically predetermined by fitting offline OCV data. Commonly used two OCV tests are compared in few literatures. Moreover, they only focus on the middle SoC region (i.e., 20% and 90%) of LiNiMnCoO2 (NMC) LiBs, the performances of these OCV tests for other battery types and entire SoC region are failed to be addressed. In this paper, the impact of two OCV tests on SoC estimation for NMC and LiFePO4 (LFP) LiBs is investigated at different temperatures and aging stages. A parameter and SoC joint estimation method is introduced, based on an integrated H∞-UKF method. The accuracy and reliability of the proposed method are verified by using two different OCV testing data at various ambient temperatures and aging stages for some commercial NMC and LFP LiBs. The results indicate that the incremental OCV test method results in more accurate SoC estimation than the low current OCV test method, on both NMC and LFP LiBs. Furthermore, to reach equilibrium states and achieve desired SoC estimation accuracy, the relaxation period in the incremental OCV test method needs to be extended at low temperatures.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:205:y:2017:i:c:p:892-902
    DOI: 10.1016/j.apenergy.2017.08.124
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    6. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
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    12. Yu Peng & Yandong Hou & Yuchen Song & Jingyue Pang & Datong Liu, 2018. "Lithium-Ion Battery Prognostics with Hybrid Gaussian Process Function Regression," Energies, MDPI, vol. 11(6), pages 1-20, June.
    13. Sung-Tae Ko & Jaehyung Lee & Jung-Hoon Ahn & Byoung Kuk Lee, 2019. "Innovative Modeling Approach for Li-Ion Battery Packs Considering Intrinsic Cell Unbalances and Packaging Elements," Energies, MDPI, vol. 12(3), pages 1-13, January.
    14. Xiao, Renxin & Hu, Yanwen & Jia, Xianguang & Chen, Guisheng, 2022. "A novel estimation of state of charge for the lithium-ion battery in electric vehicle without open circuit voltage experiment," Energy, Elsevier, vol. 243(C).
    15. Zhang, Shuzhi & Zhang, Chen & Jiang, Shiyong & Zhang, Xiongwen, 2022. "A comparative study of different adaptive extended/unscented Kalman filters for lithium-ion battery state-of-charge estimation," Energy, Elsevier, vol. 246(C).
    16. Renxin, Xiao & Yi, Yang & Xianguang, Jia & Nan, Pan, 2023. "Collaborative estimations of state of energy and maximum available energy of lithium-ion batteries with optimized time windows considering instantaneous energy efficiencies," Energy, Elsevier, vol. 274(C).
    17. Zhu, Rui & Duan, Bin & Zhang, Chenghui & Gong, Sizhao, 2019. "Accurate lithium-ion battery modeling with inverse repeat binary sequence for electric vehicle applications," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    18. Chen, Xiang & Deng, Yelin & Wang, Xingxing & Yuan, Yinnan, 2024. "The capacity degradation path prediction for the prismatic lithium-ion batteries based on the multi-features extraction with SGPR," Energy, Elsevier, vol. 299(C).

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