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An mechanical/thermal analytical model for prismatic lithium-ion cells with silicon‑carbon electrodes in charge/discharge cycles

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Listed:
  • Huang, Zhiliang
  • Wang, Huaixing
  • Gan, Zhouwang
  • Yang, Tongguang
  • Yuan, Cong
  • Lei, Bing
  • Chen, Jie
  • Wu, Shengben

Abstract

Conventional lithium-ion cell state analysis methods face challenges in applicability, efficiency, and convergence for online state evaluation of cells with silicon‑carbon electrodes. This paper proposes a mechanical/thermal analytical model for prismatic cells with silicon‑carbon electrodes to evaluate cell stress and electrode deformation during charging/discharging. A dual-layer mechanical sub-model is proposed to obtain the electrode deformations under the volumetric loads of the SOC-dependent and thermal expansions. A viscoelastic constitutive model for electrode materials is developed to capture the mechanical hysteresis effects in a constrained space. A thermal circuit sub-model is created to assess the cell temperature distribution, providing boundary conditions for calculating electrode thermal expansion. The analytical model contains a small number of input parameters and first-order differential equations. The results cover the temperature, stress, elastic and viscoelastic deformations of the electrodes. The performance of the proposed approach was validated through numerical and experimental results on two commercial cells during constant current and abrupt current cycles. The second-level efficiency, robust convergence, and refined results exhibit an excellent prospect in energy storage and vehicle power applications.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:365:y:2024:i:c:s0306261924006020
    DOI: 10.1016/j.apenergy.2024.123219
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    1. Toriello, Alejandro & Vielma, Juan Pablo, 2012. "Fitting piecewise linear continuous functions," European Journal of Operational Research, Elsevier, vol. 219(1), pages 86-95.
    2. Palacios, Anabel & Cong, Lin & Navarro, M.E. & Ding, Yulong & Barreneche, Camila, 2019. "Thermal conductivity measurement techniques for characterizing thermal energy storage materials – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 32-52.
    3. 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).
    4. Yao, Lei & Fang, Zhanpeng & Xiao, Yanqiu & Hou, Junjian & Fu, Zhijun, 2021. "An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine," Energy, Elsevier, vol. 214(C).
    5. Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Ke, Yuzhi & Qiu, Zhiqiang & Luo, Jian & Tang, Yong & Wang, Chun & Yuan, Yuhang & Huang, Yao, 2020. "A review on structuralized current collectors for high-performance lithium-ion battery anodes," Applied Energy, Elsevier, vol. 276(C).
    6. Román-Ramírez, L.A. & Marco, J., 2022. "Design of experiments applied to lithium-ion batteries: A literature review," Applied Energy, Elsevier, vol. 320(C).
    7. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    8. Shen, Boyang & Chen, Yu & Li, Chuanyue & Wang, Sheng & Chen, Xiaoyuan, 2021. "Superconducting fault current limiter (SFCL): Experiment and the simulation from finite-element method (FEM) to power/energy system software," Energy, Elsevier, vol. 234(C).
    9. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    10. 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).
    11. Barcellona, Simone & Piegari, Luigi, 2021. "Integrated electro-thermal model for pouch lithium ion batteries," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 5-19.
    12. Tan, P. & Jiang, H.R. & Zhu, X.B. & An, L. & Jung, C.Y. & Wu, M.C. & Shi, L. & Shyy, W. & Zhao, T.S., 2017. "Advances and challenges in lithium-air batteries," Applied Energy, Elsevier, vol. 204(C), pages 780-806.
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