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Development of a degradation-conscious physics-based lithium-ion battery model for use in power system planning studies

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  • Li, Yang
  • Vilathgamuwa, Mahinda
  • Choi, San Shing
  • Farrell, Troy W.
  • Tran, Ngoc Tham
  • Teague, Joseph

Abstract

A computationally-efficient and reliable method is developed to permit the simultaneous assessment of both the short- and long-term performance of lithium-ion battery in power system planning studies. Toward this end, a physics-based equivalent circuit model of the lithium-ion battery is derived in which side reaction-induced degradation of the battery is included. Whence a computational procedure is developed to enable the parametric values of the circuit elements in the equivalent circuit model to be automatically updated as the battery operates. The resulting model allows the increase in the internal resistance and the decrease in the energy storage capacity of the battery to be determined, based solely on the information of the power flows at the battery terminals. Dynamic simulation results obtained using the developed equivalent circuit model are shown to be in close agreement with those obtained from well-established electrochemical models, but at a much reduced computational burden.

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  • Li, Yang & Vilathgamuwa, Mahinda & Choi, San Shing & Farrell, Troy W. & Tran, Ngoc Tham & Teague, Joseph, 2019. "Development of a degradation-conscious physics-based lithium-ion battery model for use in power system planning studies," Applied Energy, Elsevier, vol. 248(C), pages 512-525.
    • Handle: RePEc:eee:appene:v:248:y:2019:i:c:p:512-525
    DOI: 10.1016/j.apenergy.2019.04.143
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    References listed on IDEAS

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    Cited by:

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    2. Wei, Zhongbao & Zhao, Difan & He, Hongwen & Cao, Wanke & Dong, Guangzhong, 2020. "A noise-tolerant model parameterization method for lithium-ion battery management system," Applied Energy, Elsevier, vol. 268(C).
    3. Christophe Savard & Emiliia Iakovleva & Daniil Ivanchenko & Anton Rassõlkin, 2021. "Accessible Battery Model with Aging Dependency," Energies, MDPI, vol. 14(12), pages 1-16, June.
    4. Li, Changlong & Cui, Naxin & Chang, Long & Cui, Zhongrui & Yuan, Haitao & Zhang, Chenghui, 2022. "Effect of parallel connection topology on air-cooled lithium-ion battery module: Inconsistency analysis and comprehensive evaluation," Applied Energy, Elsevier, vol. 313(C).
    5. Zhao, Xinyi & Shen, Xinwei & Guo, Qinglai & Sun, Hongbin & Oren, Shmuel S., 2020. "A stochastic distribution system planning method considering regulation services and energy storage degradation," Applied Energy, Elsevier, vol. 277(C).
    6. 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).
    7. Maheshwari, Arpit & Paterakis, Nikolaos G. & Santarelli, Massimo & Gibescu, Madeleine, 2020. "Optimizing the operation of energy storage using a non-linear lithium-ion battery degradation model," Applied Energy, Elsevier, vol. 261(C).
    8. Li, Yang & Vilathgamuwa, Mahinda & Choi, San Shing & Xiong, Binyu & Tang, Jinrui & Su, Yixin & Wang, Yu, 2020. "Design of minimum cost degradation-conscious lithium-ion battery energy storage system to achieve renewable power dispatchability," Applied Energy, Elsevier, vol. 260(C).
    9. Li, Changlong & Cui, Naxin & Wang, Chunyu & Zhang, Chenghui, 2021. "Reduced-order electrochemical model for lithium-ion battery with domain decomposition and polynomial approximation methods," Energy, Elsevier, vol. 221(C).
    10. Vykhodtsev, Anton V. & Jang, Darren & Wang, Qianpu & Rosehart, William & Zareipour, Hamidreza, 2022. "A review of modelling approaches to characterize lithium-ion battery energy storage systems in techno-economic analyses of power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).

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