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State of charge estimation for lithium-ion pouch batteries based on stress measurement

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  • Dai, Haifeng
  • Yu, Chenchen
  • Wei, Xuezhe
  • Sun, Zechang

Abstract

State of charge (SOC) estimation is one of the important tasks of battery management system (BMS). Being different from other researches, a novel method of SOC estimation for pouch lithium-ion battery cells based on stress measurement is proposed. With a comprehensive experimental study, we find that, the stress of the battery during charge/discharge is composed of the static stress and the dynamic stress. The static stress, which is the measured stress in equilibrium state, corresponds to SOC, this phenomenon facilitates the design of our stress-based SOC estimation. The dynamic stress, on the other hand, is influenced by multiple factors including charge accumulation or depletion, current and historical operation, thus a multiple regression model of the dynamic stress is established. Based on the relationship between static stress and SOC, as well as the dynamic stress modeling, the SOC estimation method is founded. Experimental results show that the stress-based method performs well with a good accuracy, and this method offers a novel perspective for SOC estimation.

Suggested Citation

  • Dai, Haifeng & Yu, Chenchen & Wei, Xuezhe & Sun, Zechang, 2017. "State of charge estimation for lithium-ion pouch batteries based on stress measurement," Energy, Elsevier, vol. 129(C), pages 16-27.
    • Handle: RePEc:eee:energy:v:129:y:2017:i:c:p:16-27
    DOI: 10.1016/j.energy.2017.04.099
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    References listed on IDEAS

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    5. Jiang, Yihui & Xu, Jun & Hou, Wenlong & Mei, Xuesong, 2021. "A stack pressure based equivalent mechanical model of lithium-ion pouch batteries," Energy, Elsevier, vol. 221(C).
    6. Bian, Xiaolei & Liu, Longcheng & Yan, Jinying, 2019. "A model for state-of-health estimation of lithium ion batteries based on charging profiles," Energy, Elsevier, vol. 177(C), pages 57-65.
    7. Jiang, Yihui & Xu, Jun & Liu, Mengmeng & Mei, Xuesong, 2022. "An electromechanical coupling model-based state of charge estimation method for lithium-ion pouch battery modules," Energy, Elsevier, vol. 259(C).
    8. Cui, Yingzhi & Zuo, Pengjian & Du, Chunyu & Gao, Yunzhi & Yang, Jie & Cheng, Xinqun & Ma, Yulin & Yin, Geping, 2018. "State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method," Energy, Elsevier, vol. 144(C), pages 647-656.
    9. Wu, Jian & Wang, Xiangyu & Li, Liang & Qin, Cun'an & Du, Yongchang, 2018. "Hierarchical control strategy with battery aging consideration for hybrid electric vehicle regenerative braking control," Energy, Elsevier, vol. 145(C), pages 301-312.
    10. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    11. Li, Yanwen & Wang, Chao & Gong, Jinfeng, 2017. "A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique," Energy, Elsevier, vol. 141(C), pages 1402-1415.
    12. Lysander De Sutter & Gert Berckmans & Mario Marinaro & Jelle Smekens & Yousef Firouz & Margret Wohlfahrt-Mehrens & Joeri Van Mierlo & Noshin Omar, 2018. "Comprehensive Aging Analysis of Volumetric Constrained Lithium-Ion Pouch Cells with High Concentration Silicon-Alloy Anodes," Energies, MDPI, vol. 11(11), pages 1-21, October.
    13. 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.
    14. Muhammad Umair Ali & Muhammad Ahmad Kamran & Pandiyan Sathish Kumar & Himanshu & Sarvar Hussain Nengroo & Muhammad Adil Khan & Altaf Hussain & Hee-Je Kim, 2018. "An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method," Energies, MDPI, vol. 11(11), pages 1-19, October.

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