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A stack pressure based equivalent mechanical model of lithium-ion pouch batteries

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  • Jiang, Yihui
  • Xu, Jun
  • Hou, Wenlong
  • Mei, Xuesong

Abstract

The stack level mechanical pressure will be inevitably generated among the battery cells, which dramatically affects the battery properties. To accurately characterize the battery performance with such aspects, an equivalent mechanical model with full consideration of stack pressure is proposed in this paper. Consisting of a small amount of simple mechanical components, the proposed model is simple but accurate, which is suitable for real time applications. By measuring the stack pressure, the battery thickness can be predicted by the proposed model, and accordingly, the battery states can be estimated. So the proposed equivalent mechanical model provides a potential use of the force signal based battery state estimation. The characteristics of the battery thickness variations caused by the applied force are first analyzed and the equivalent mechanical model is proposed. Furthermore, the coupling of stack pressure and the battery thickness is incorporated to the proposed model. Then a reliable parameter identification method is proposed to obtain the model parameters under different conditions. On this basis, the accuracy and the adaptability of the proposed model are experimentally validated.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544221000530
    DOI: 10.1016/j.energy.2021.119804
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    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. Jiang, Bo & Dai, Haifeng & Wei, Xuezhe, 2020. "Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition," Applied Energy, Elsevier, vol. 269(C).
    4. Zubi, Ghassan & Dufo-López, Rodolfo & Carvalho, Monica & Pasaoglu, Guzay, 2018. "The lithium-ion battery: State of the art and future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 292-308.
    5. Ximing Cheng & Michael Pecht, 2017. "In Situ Stress Measurement Techniques on Li-ion Battery Electrodes: A Review," Energies, MDPI, vol. 10(5), pages 1-19, April.
    6. Ren, Hongbin & Zhao, Yuzhuang & Chen, Sizhong & Wang, Taipeng, 2019. "Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation," Energy, Elsevier, vol. 166(C), pages 908-917.
    7. Yang, Sheng & Wang, Wenwei & Lin, Cheng & Shen, Weixiang & Li, Yiding, 2019. "Improved constitutive model of the jellyroll for cylindrical lithium ion batteries considering microscopic damage," Energy, Elsevier, vol. 185(C), pages 202-212.
    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. Golriz Kermani & Elham Sahraei, 2017. "Review: Characterization and Modeling of the Mechanical Properties of Lithium-Ion Batteries," Energies, MDPI, vol. 10(11), pages 1-25, October.
    10. 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.
    11. He, Hongwen & Zhang, Xiaowei & Xiong, Rui & Xu, Yongli & Guo, Hongqiang, 2012. "Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles," Energy, Elsevier, vol. 39(1), pages 310-318.
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    Cited by:

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    3. Huang, Zhiliang & Wang, Huaixing & Zou, Wei & Zhang, Rongchuan & Wang, Yuhan & Chen, Jie & Wu, Shengben, 2024. "An online evaluation model for mechanical/thermal states in prismatic lithium-ion batteries under fast charging/discharging," Energy, Elsevier, vol. 302(C).
    4. Huang, Zhiliang & Wang, Huaixing & Yang, Tongguang & Chen, Zeye & Li, Hangyang & Chen, Jie & Wu, Shengben, 2023. "An efficient multi-state evaluation approach for lithium-ion pouch cells under dynamic conditions in pressure/current/temperature," Applied Energy, Elsevier, vol. 340(C).
    5. Huang, Zhiliang & Wang, Huaixing & Gan, Zhouwang & Yang, Tongguang & Yuan, Cong & Lei, Bing & Chen, Jie & Wu, Shengben, 2024. "An mechanical/thermal analytical model for prismatic lithium-ion cells with silicon‑carbon electrodes in charge/discharge cycles," Applied Energy, Elsevier, vol. 365(C).
    6. Shi, Mingjie & Xu, Jun & Lin, Chuanping & Mei, Xuesong, 2022. "A fast state-of-health estimation method using single linear feature for lithium-ion batteries," Energy, Elsevier, vol. 256(C).
    7. Ziming Xu & Jun Xu & Zhechen Guo & Haitao Wang & Zheng Sun & Xuesong Mei, 2022. "Design and Optimization of a Novel Microchannel Battery Thermal Management System Based on Digital Twin," Energies, MDPI, vol. 15(4), pages 1-20, February.
    8. Yi, Yahui & Xia, Chengyu & Shi, Lei & Meng, Leifeng & Chi, Qifu & Qian, Liqin & Ma, Tiancai & Chen, Siqi, 2024. "Lithium-ion battery expansion mechanism and Gaussian process regression based state of charge estimation with expansion characteristics," Energy, Elsevier, vol. 292(C).

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