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Construction of electrochemical model for high C-rate conditions in lithium-ion battery based on experimental analogy method

Author

Listed:
  • Wang, Limei
  • Jin, Mengjie
  • Cai, Yingfeng
  • Lian, Yubo
  • Zhao, Xiuliang
  • Wang, Ruochen
  • Qiao, Sibing
  • Chen, Long
  • Yan, Xueqing

Abstract

Lithium-ion batteries (LIBs) modeling is critical for the safe and efficient operation of electric vehicles (EVs) and energy storage systems (BESSs). Most electrochemical models are mainly suitable for normal temperature or low C-rate conditions (≤2 C). Meanwhile, the electrochemical model parameters are usually obtained by the half-cell testing method. This method has difficulty in disassembling batteries and obtaining model parameters quickly. To solve this problem, the internal electrochemical behavior of LIBs under high-C rate conditions is first studied in this paper. Through the above theoretical analysis, the relationship between the solid phase diffusion coefficient, the reaction rate constant and temperature/concentration must be considered under high C-rate conditions. Then a fast calibration method for electrochemical model parameters is proposed, and a variable parameter high C-rate model is established. Finally, the variable parameter high C-rate model is validated at different rates of 5 °C, 25 °C and 55 °C. The results show that the mean absolute errors (MAE) of the variable parameter high C-rate model are within 13 mV under low C-rate conditions. Under the high C-rate condition (≤6 C), the MAEs of the model are within 50 mV.

Suggested Citation

  • Wang, Limei & Jin, Mengjie & Cai, Yingfeng & Lian, Yubo & Zhao, Xiuliang & Wang, Ruochen & Qiao, Sibing & Chen, Long & Yan, Xueqing, 2023. "Construction of electrochemical model for high C-rate conditions in lithium-ion battery based on experimental analogy method," Energy, Elsevier, vol. 279(C).
    • Handle: RePEc:eee:energy:v:279:y:2023:i:c:s0360544223014676
    DOI: 10.1016/j.energy.2023.128073
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    References listed on IDEAS

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    1. Tanim, Tanvir R. & Rahn, Christopher D. & Wang, Chao-Yang, 2015. "State of charge estimation of a lithium ion cell based on a temperature dependent and electrolyte enhanced single particle model," Energy, Elsevier, vol. 80(C), pages 731-739.
    2. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    3. 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.
    4. Byoungwoo Kang & Gerbrand Ceder, 2009. "Battery materials for ultrafast charging and discharging," Nature, Nature, vol. 458(7235), pages 190-193, March.
    5. Xiong, Rui & Li, Linlin & Li, Zhirun & Yu, Quanqing & Mu, Hao, 2018. "An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application," Applied Energy, Elsevier, vol. 219(C), pages 264-275.
    6. Wang, Yujie & Chen, Zonghai & Zhang, Chenbin, 2017. "On-line remaining energy prediction: A case study in embedded battery management system," Applied Energy, Elsevier, vol. 194(C), pages 688-695.
    7. Li, J. & Adewuyi, K. & Lotfi, N. & Landers, R.G. & Park, J., 2018. "A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation," Applied Energy, Elsevier, vol. 212(C), pages 1178-1190.
    8. Ma, Wentao & Guo, Peng & Wang, Xiaofei & Zhang, Zhiyu & Peng, Siyuan & Chen, Badong, 2022. "Robust state of charge estimation for Li-ion batteries based on cubature kalman filter with generalized maximum correntropy criterion," Energy, Elsevier, vol. 260(C).
    9. Alice J. Merryweather & Christoph Schnedermann & Quentin Jacquet & Clare P. Grey & Akshay Rao, 2021. "Operando optical tracking of single-particle ion dynamics in batteries," Nature, Nature, vol. 594(7864), pages 522-528, June.
    10. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
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