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Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery

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  • Deng, Zhongwei
  • Deng, Hao
  • Yang, Lin
  • Cai, Yishan
  • Zhao, Xiaowei

Abstract

Physics-based models for lithium-ion battery have been regarded as a promising alternative to equivalent circuit models due to their ability to describe internal electrochemical states of battery. However, the huge computational burden and numerous parameters of these models impede their application in embedded battery management system. To deal with the above problem, a reduced-order physics-based model for lithium-ion battery with better tradeoff between the model fidelity and computational complexity is developed. A strategy is proposed to extend the operation from a fixed point to full state of charge range. As the model consists of constant, varying, identifiable and unidentifiable parameters, it is impractical to identify the full set of parameters only using the current-voltage data. To sort out the identifiable parameters, a criterion based on calculating the determinant and condition number of Fisher information matrix (FIM) is employed. A subset with maximum nine identifiable parameters is obtained and then identified by nonlinear least square regression algorithm with confidence region calculated by FIM. Compared with the outputs from commercial software, the effectiveness of the battery model and extending strategy are verified. The estimated parameters deviate from the true values slightly, and produce small voltage errors at different current profiles.

Suggested Citation

  • Deng, Zhongwei & Deng, Hao & Yang, Lin & Cai, Yishan & Zhao, Xiaowei, 2017. "Implementation of reduced-order physics-based model and multi-parameters identification strategy for lithium-ion battery," Energy, Elsevier, vol. 138(C), pages 509-519.
    • Handle: RePEc:eee:energy:v:138:y:2017:i:c:p:509-519
    DOI: 10.1016/j.energy.2017.07.069
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    References listed on IDEAS

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    6. 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).
    7. Deng, Zhongwei & Yang, Lin & Deng, Hao & Cai, Yishan & Li, Dongdong, 2018. "Polynomial approximation pseudo-two-dimensional battery model for online application in embedded battery management system," Energy, Elsevier, vol. 142(C), pages 838-850.
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    9. Deng, Zhongwei & Hu, Xiaosong & Lin, Xianke & Che, Yunhong & Xu, Le & Guo, Wenchao, 2020. "Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression," Energy, Elsevier, vol. 205(C).
    10. Jagdesh Kumar & Chethan Parthasarathy & Mikko Västi & Hannu Laaksonen & Miadreza Shafie-Khah & Kimmo Kauhaniemi, 2020. "Sizing and Allocation of Battery Energy Storage Systems in Åland Islands for Large-Scale Integration of Renewables and Electric Ferry Charging Stations," Energies, MDPI, vol. 13(2), pages 1-23, January.
    11. Hong-Keun Kim & Kyu-Jin Lee, 2020. "Scale-Up of Physics-Based Models for Predicting Degradation of Large Lithium Ion Batteries," Sustainability, MDPI, vol. 12(20), pages 1-18, October.
    12. Berrueta, Alberto & Urtasun, Andoni & Ursúa, Alfredo & Sanchis, Pablo, 2018. "A comprehensive model for lithium-ion batteries: From the physical principles to an electrical model," Energy, Elsevier, vol. 144(C), pages 286-300.
    13. Xu, Meng & Wang, Xia & Zhang, Liwen & Zhao, Peng, 2021. "Comparison of the effect of linear and two-step fast charging protocols on degradation of lithium ion batteries," Energy, Elsevier, vol. 227(C).

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