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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

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    Cited by:

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    2. 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).
    3. 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).
    4. 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).
    5. 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).
    6. 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.
    7. 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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