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Determination of half-cell open-circuit potential curve of silicon-graphite in a physics-based model for lithium-ion batteries

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  • Gao, Yizhao
  • Sun, Ziqiang
  • Zhang, Dong
  • Shi, Dapai
  • Zhang, Xi

Abstract

Lithium-ion batteries with silicon/graphite anodes have the potential to deliver high theoretical capacity. However, these electrodes exhibit significant hysteresis, which presents challenges in accurately estimating the open-circuit potentials (OCP) of the electrodes within a physics-based model. This paper proposes a method to establish the relationship between the electrode OCP and stoichiometry. Galvanostatic intermittent titration technique (GITT) tests are performed on half-cells to measure the charge and discharge OCP. To account for hysteresis, a hysteresis factor is defined to balance the lithiation and de-lithiation OCP. The estimated open-circuit voltage (OCV) of the full-cell is obtained by subtracting the anode OCP from the cathode OCP. The OCP and hysteresis factor are then optimized by minimizing the error between the measured OCV and the estimated OCV. Two different OCV test methods, namely the incremental method and C/30 galvanostatic method, are compared. The OCV estimation for fresh cells shows good agreement with experimental values, with root-mean-square errors (RMSEs) below 6.682 mV. To evaluate the effectiveness of the obtained OCPs in the full-cell model, the optimized OCPs are incorporated into the physics-based model. Under the Hybrid Pulse Power Characterization (HPPC) test, the electrochemical model utilizing the optimized OCP with the incremental OCV and C/30 galvanostatic OCV exhibits RMSEs of 10.587 mV and 11.016 mV, respectively, in predicting the cell voltage. Finally, the OCP identification method is assessed with cells at different aging states. The OCV predictions for degraded cells maintain RMSEs below 9.074 mV, thus validating the effectiveness of the developed OCP estimation method.

Suggested Citation

  • Gao, Yizhao & Sun, Ziqiang & Zhang, Dong & Shi, Dapai & Zhang, Xi, 2023. "Determination of half-cell open-circuit potential curve of silicon-graphite in a physics-based model for lithium-ion batteries," Applied Energy, Elsevier, vol. 349(C).
  • Handle: RePEc:eee:appene:v:349:y:2023:i:c:s0306261923009856
    DOI: 10.1016/j.apenergy.2023.121621
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    References listed on IDEAS

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    1. Wang, Yu & Ren, Dongsheng & Feng, Xuning & Wang, Li & Ouyang, Minggao, 2022. "Thermal runaway modeling of large format high-nickel/silicon-graphite lithium-ion batteries based on reaction sequence and kinetics," Applied Energy, Elsevier, vol. 306(PA).
    2. Wei, Zhongbao & Hu, Jian & Li, Yang & He, Hongwen & Li, Weihan & Sauer, Dirk Uwe, 2022. "Hierarchical soft measurement of load current and state of charge for future smart lithium-ion batteries," Applied Energy, Elsevier, vol. 307(C).
    3. Bi, Yalan & Choe, Song-Yul, 2020. "An adaptive sigma-point Kalman filter with state equality constraints for online state-of-charge estimation of a Li(NiMnCo)O2/Carbon battery using a reduced-order electrochemical model," Applied Energy, Elsevier, vol. 258(C).
    4. Tian, Jinpeng & Xiong, Rui & Shen, Weixiang & Lu, Jiahuan, 2021. "State-of-charge estimation of LiFePO4 batteries in electric vehicles: A deep-learning enabled approach," Applied Energy, Elsevier, vol. 291(C).
    5. Gao, Yizhao & Liu, Chenghao & Chen, Shun & Zhang, Xi & Fan, Guodong & Zhu, Chong, 2022. "Development and parameterization of a control-oriented electrochemical model of lithium-ion batteries for battery-management-systems applications," Applied Energy, Elsevier, vol. 309(C).
    6. Sturm, J. & Ennifar, H. & Erhard, S.V. & Rheinfeld, A. & Kosch, S. & Jossen, A., 2018. "State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter," Applied Energy, Elsevier, vol. 223(C), pages 103-123.
    7. Yang, Jufeng & Huang, Wenxin & Xia, Bing & Mi, Chris, 2019. "The improved open-circuit voltage characterization test using active polarization voltage reduction method," Applied Energy, Elsevier, vol. 237(C), pages 682-694.
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