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Unlocking electrochemical model-based online power prediction for lithium-ion batteries via Gaussian process regression

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  • Li, Weihan
  • Fan, Yue
  • Ringbeck, Florian
  • Jöst, Dominik
  • Sauer, Dirk Uwe

Abstract

The knowledge of the dynamic available charging and discharging power of the battery is a piece of essential information for the safety and longevity of the battery energy storage systems. An accurate real-time prediction of these quantities is very challenging due to the high nonlinearities of battery dynamics. In this paper, an electrochemical model-based online state-of-power prediction algorithm under different time horizons is developed for a safer and more reliable operation of lithium-ion batteries. The safety constraints, which define the safety operation area for the power prediction, are designed based on not only the terminal voltage but also battery internal electrochemical states, i.e., the electrode surface concentration, the electrolyte concentration, and the side reaction overpotential. The algorithm is validated by simulations and experiments under a dynamic load profile, and the dominating constraints in charging and discharging as well as the influence of predictive time horizons on the available battery power are analyzed, providing important information for further researches. Furthermore, the computational speed of the proposed iterative algorithm is improved with the integration of Gaussian process regression by up to 50%. A comparative study with a state-of-the-art equivalent circuit model-based approach highlights the significant benefits of the proposed electrochemical model-based algorithm in operation safety enhancement and battery performance improvement.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pb:s0306261921013933
    DOI: 10.1016/j.apenergy.2021.118114
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    References listed on IDEAS

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    Cited by:

    1. Liming Deng & Wenjing Shen & Kangkang Xu & Xuhui Zhang, 2024. "An Adaptive Modeling Method for the Prognostics of Lithium-Ion Batteries on Capacity Degradation and Regeneration," Energies, MDPI, vol. 17(7), pages 1-15, April.
    2. Chen, Zhang & Chen, Liqun & Ma, Zhengwei & Xu, Kangkang & Zhou, Yu & Shen, Wenjing, 2023. "Joint modeling for early predictions of Li-ion battery cycle life and degradation trajectory," Energy, Elsevier, vol. 277(C).
    3. Wang, Cong & Chen, Yunxia, 2024. "Unsupervised dynamic prognostics for abnormal degradation of lithium-ion battery," Applied Energy, Elsevier, vol. 365(C).
    4. Li, Kuo & Gao, Xiao & Liu, Caixia & Chang, Chun & Li, Xiaoyu, 2023. "A novel Co-estimation framework of state-of-charge, state-of-power and capacity for lithium-ion batteries using multi-parameters fusion method," Energy, Elsevier, vol. 269(C).
    5. Shi, Haotian & Wang, Shunli & Fernandez, Carlos & Yu, Chunmei & Xu, Wenhua & Dablu, Bobobee Etse & Wang, Liping, 2022. "Improved multi-time scale lumped thermoelectric coupling modeling and parameter dispersion evaluation of lithium-ion batteries," Applied Energy, Elsevier, vol. 324(C).
    6. Zhu, Yuli & Jiang, Bo & Zhu, Jiangong & Wang, Xueyuan & Wang, Rong & Wei, Xuezhe & Dai, Haifeng, 2023. "Adaptive state of health estimation for lithium-ion batteries using impedance-based timescale information and ensemble learning," Energy, Elsevier, vol. 284(C).
    7. Wang, Cong & Chen, Yunxia & Zhang, Qingyuan & Zhu, Jiaxiao, 2023. "Dynamic early recognition of abnormal lithium-ion batteries before capacity drops using self-adaptive quantum clustering," Applied Energy, Elsevier, vol. 336(C).
    8. Jiang, Bo & Zhu, Yuli & Zhu, Jiangong & Wei, Xuezhe & Dai, Haifeng, 2023. "An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range," Energy, Elsevier, vol. 263(PC).
    9. Jiang, Junyu & Yu, Yuanbin & Min, Haitao & Cao, Qiming & Sun, Weiyi & Zhang, Zhaopu & Luo, Chunqi, 2023. "Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression," Energy, Elsevier, vol. 263(PD).

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