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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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    Cited by:

    1. He, Tengfei & Zhang, Teng & Wang, Zhirong & Cai, Qiong, 2022. "A comprehensive numerical study on electrochemical-thermal models of a cylindrical lithium-ion battery during discharge process," Applied Energy, Elsevier, vol. 313(C).
    2. Wang, Chao & Zhang, Xu & Cui, Yixiu & He, Ke & Cao, Yong & Liu, Xiaojiang & Zeng, Chao, 2022. "A system-level thermal-electrochemical coupled model for evaluating the activation process of thermal batteries," Applied Energy, Elsevier, vol. 328(C).
    3. Xu, Wenqiang & Wu, Xiaogang & Li, Yalun & Wang, Hewu & Lu, Languang & Ouyang, Minggao, 2023. "A comprehensive review of DC arc faults and their mechanisms, detection, early warning strategies, and protection in battery systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 186(C).
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    5. 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.
    6. Yin, Xiong & Wen, Kai & Huang, Weihe & Luo, Yinwei & Ding, Yi & Gong, Jing & Gao, Jianfeng & Hong, Bingyuan, 2023. "A high-accuracy online transient simulation framework of natural gas pipeline network by integrating physics-based and data-driven methods," Applied Energy, Elsevier, vol. 333(C).
    7. 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.
    8. 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).
    9. Hend M. Fahmy & Rania A. Sweif & Hany M. Hasanien & Marcos Tostado-Véliz & Mohammed Alharbi & Francisco Jurado, 2023. "Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm," Mathematics, MDPI, vol. 11(9), pages 1-31, May.
    10. 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).
    11. 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.
    12. Du, Jingcai & Zhang, Caiping & Li, Shuowei & Zhang, Linjing & Zhang, Weige, 2024. "Two-stage prediction method for capacity aging trajectories of lithium-ion batteries based on Siamese-convolutional neural network," Energy, Elsevier, vol. 295(C).
    13. 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).
    14. 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).
    15. Lai, Qingzhi & Ahn, Hyoung Jun & Kim, YoungJin & Kim, You Na & Lin, Xinfan, 2021. "New data optimization framework for parameter estimation under uncertainties with application to lithium-ion battery," Applied Energy, Elsevier, vol. 295(C).
    16. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(C).
    17. Lai, Xin & Yao, Yi & Tang, Xiaopeng & Zheng, Yuejiu & Zhou, Yuanqiang & Sun, Yuedong & Gao, Furong, 2023. "Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions," Energy, Elsevier, vol. 282(C).
    18. Liu, Qiquan & Ma, Jian & Zhao, Xuan & Zhang, Kai & Xiangli, Kang & Meng, Dean, 2024. "A novel method for fault diagnosis and type identification of cell voltage inconsistency in electric vehicles using weighted Euclidean distance evaluation and statistical analysis," Energy, Elsevier, vol. 293(C).
    19. Liu, Yongjie & Huang, Zhiwu & Wu, Yue & Yan, Lisen & Jiang, Fu & Peng, Jun, 2022. "An online hybrid estimation method for core temperature of Lithium-ion battery with model noise compensation," Applied Energy, Elsevier, vol. 327(C).
    20. Wang, Qiao & Ye, Min & Wei, Meng & Lian, Gaoqi & Li, Yan, 2023. "Deep convolutional neural network based closed-loop SOC estimation for lithium-ion batteries in hierarchical scenarios," Energy, Elsevier, vol. 263(PB).
    21. Haobin Jiang & Xijia Chen & Yifu Liu & Qian Zhao & Huanhuan Li & Biao Chen, 2021. "Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O 2 Battery," Energies, MDPI, vol. 14(2), pages 1-19, January.
    22. 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).
    23. Khosravi, Nima & Dowlatabadi, Masrour & Abdelghany, Muhammad Bakr & Tostado-Véliz, Marcos & Jurado, Francisco, 2024. "Enhancing battery management for HEVs and EVs: A hybrid approach for parameter identification and voltage estimation in lithium-ion battery models," Applied Energy, Elsevier, vol. 356(C).
    24. Chein-Chung Sun & Chun-Hung Chou & Yu-Liang Lin & Yu-Hua Huang, 2022. "A Cost-Effective Passive/Active Hybrid Equalizer Circuit Design," Energies, MDPI, vol. 15(6), pages 1-20, March.

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