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Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries

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  • Li, Weihan
  • Cao, Decheng
  • Jöst, Dominik
  • Ringbeck, Florian
  • Kuipers, Matthias
  • Frie, Fabian
  • Sauer, Dirk Uwe

Abstract

Accurate identification of physical parameters of a lithium-ion electrochemical model is of critical importance for next-generation battery management systems. The complexity of the electrochemical model increases the difficulty of the identification process, and hence the analysis of parameter identifiability is the cornerstone for accurate parameter identification. The overarching goal of this paper is to analyze the parameter sensitivity of an electrochemical model under both the charging process and real-world driving cycles. The boundaries for the sensitivity analysis of 26 physical parameters are determined with a systematic benchmarking of published parameters for lithium Nickel-Manganese-Cobalt-Oxide/graphite cells. In particular, the sensitivity of the parameters is analyzed not only for terminal voltage but also for essential states in an electrochemical model-based battery management system, e.g., cathode bulk state of charge, cathode surface state of charge and anode potential. The sensitivity matrices of the parameters under different C-rates and depth of discharge regions clearly show their different influences on capacity-related parameters and other parameters. Furthermore, the rankings of the normalized parameter sensitivity index provide us the identifiability of the parameters, as well as the influence of parameter inaccuracy on the main functionalities in an electrochemical model-based battery management system.

Suggested Citation

  • Li, Weihan & Cao, Decheng & Jöst, Dominik & Ringbeck, Florian & Kuipers, Matthias & Frie, Fabian & Sauer, Dirk Uwe, 2020. "Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries," Applied Energy, Elsevier, vol. 269(C).
    • Handle: RePEc:eee:appene:v:269:y:2020:i:c:s0306261920306164
    DOI: 10.1016/j.apenergy.2020.115104
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    2. Miquel Martí-Florences & Andreu Cecilia & Ramon Costa-Castelló, 2023. "Modelling and Estimation in Lithium-Ion Batteries: A Literature Review," Energies, MDPI, vol. 16(19), pages 1-36, September.
    3. Gu, Yuxuan & Wang, Jianxiao & Chen, Yuanbo & Xiao, Wei & Deng, Zhongwei & Chen, Qixin, 2023. "A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control," Energy, Elsevier, vol. 264(C).
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    12. Chenqiang Luo & Zhendong Zhang & Shunliang Zhu & Yongying Li, 2023. "State-of-Health Prediction of Lithium-Ion Batteries Based on Diffusion Model with Transfer Learning," Energies, MDPI, vol. 16(9), pages 1-14, April.
    13. Sadiqa Jafari & Zeinab Shahbazi & Yung-Cheol Byun & Sang-Joon Lee, 2022. "Lithium-Ion Battery Estimation in Online Framework Using Extreme Gradient Boosting Machine Learning Approach," Mathematics, MDPI, vol. 10(6), pages 1-17, March.
    14. Dai, Haifeng & Jiang, Bo & Hu, Xiaosong & Lin, Xianke & Wei, Xuezhe & Pecht, Michael, 2021. "Advanced battery management strategies for a sustainable energy future: Multilayer design concepts and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
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    16. Li, Weihan & Fan, Yue & Ringbeck, Florian & Jöst, Dominik & Sauer, Dirk Uwe, 2022. "Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression," Applied Energy, Elsevier, vol. 306(PB).
    17. Song, Minseok & Choe, Song-Yul, 2022. "Parameter sensitivity analysis of a reduced-order electrochemical-thermal model for heat generation rate of lithium-ion batteries," Applied Energy, Elsevier, vol. 305(C).
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    19. 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).
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