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An electromechanical coupling model-based state of charge estimation method for lithium-ion pouch battery modules

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  • Jiang, Yihui
  • Xu, Jun
  • Liu, Mengmeng
  • Mei, Xuesong

Abstract

The performance of state of charge (SOC) estimation can be improved by using multi-dimensional signals. However, conventional battery models developed for SOC estimation have difficulties in simulating the coupling relationship between the mechanical and electrical characteristics. In this paper, an electromechanical coupling model (EmCM) of a lithium-ion pouch battery module is established for SOC estimation in real-time. To achieve the closed-loop SOC estimation based on force signal feedback, the stack pressure is chosen as the model output. The current and SOC are set as the model input and a state variable, respectively. On this basis, a novel SOC estimation method through current and stack pressure is proposed. The model parameters are identified by a genetic algorithm, and SOC estimation is performed using the Extended Kalman filter algorithm. The experiment results indicate that the proposed EmCM can depict the stack pressure variations with high accuracy. The SOC estimation error can be controlled within ±2.8% for both Li [NiCoMn]O2 cells and LiFePO4 cells.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:259:y:2022:i:c:s0360544222019168
    DOI: 10.1016/j.energy.2022.125019
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    References listed on IDEAS

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    1. Miriam A. Figueroa-Santos & Jason B. Siegel & Anna G. Stefanopoulou, 2020. "Leveraging Cell Expansion Sensing in State of Charge Estimation: Practical Considerations," Energies, MDPI, vol. 13(10), pages 1-24, May.
    2. Xu, Jun & Wang, Haitao & Shi, Hu & Mei, Xuesong, 2020. "Multi-scale short circuit resistance estimation method for series connected battery strings," Energy, Elsevier, vol. 202(C).
    3. Xiao Wang & Jun Xu & Yunfei Zhao, 2018. "Wavelet Based Denoising for the Estimation of the State of Charge for Lithium-Ion Batteries," Energies, MDPI, vol. 11(5), pages 1-13, May.
    4. Hannan, M.A. & Lipu, M.S.H. & Hussain, A. & Mohamed, A., 2017. "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 834-854.
    5. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.
    6. Lin, Chuanping & Xu, Jun & Shi, Mingjie & Mei, Xuesong, 2022. "Constant current charging time based fast state-of-health estimation for lithium-ion batteries," Energy, Elsevier, vol. 247(C).
    7. 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.
    8. 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.
    9. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe & Xu, Tianjiao, 2019. "Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
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    1. Liu, Mengmeng & Xu, Jun & Jiang, Yihui & Mei, Xuesong, 2023. "Multi-dimensional features based data-driven state of charge estimation method for LiFePO4 batteries," Energy, Elsevier, vol. 274(C).

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