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A computationally efficient implementation of a full and reduced-order electrochemistry-based model for Li-ion batteries

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  • Xia, L.
  • Najafi, E.
  • Li, Z.
  • Bergveld, H.J.
  • Donkers, M.C.F.

Abstract

Lithium-ion batteries are commonly employed in various applications owing to high energy density and long service life. Lithium-ion battery models are used for analysing batteries and enabling power control in applications. The Doyle-Fuller-Newman (DFN) model is a popular electrochemistry-based lithium-ion battery model which represents solid-state and electrolyte diffusion dynamics and accurately predicts the current/voltage response using a set of nonlinear partial differential equations. However, implementation of the full DFN model requires significant computation time. This paper proposes a computationally efficient implementation of the full DFN battery model, which is convenient for real-time applications. The proposed implementation is based on applying model order reduction to a spatial and temporal discretisation of the governing model equations. For model order reduction, we apply proper orthogonal decomposition and discrete empirical interpolation method, which leads to a set of reduced order nonlinear algebraic equations. These equations are solved using a particular numerical scheme, based on a damped Newton’s method. In a simulation study, the computational efficiency of the proposed implementation is shown and the resulting accuracy is presented.

Suggested Citation

  • Xia, L. & Najafi, E. & Li, Z. & Bergveld, H.J. & Donkers, M.C.F., 2017. "A computationally efficient implementation of a full and reduced-order electrochemistry-based model for Li-ion batteries," Applied Energy, Elsevier, vol. 208(C), pages 1285-1296.
    • Handle: RePEc:eee:appene:v:208:y:2017:i:c:p:1285-1296
    DOI: 10.1016/j.apenergy.2017.09.025
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    References listed on IDEAS

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    1. Zhang, Caiping & Jiang, Jiuchun & Gao, Yang & Zhang, Weige & Liu, Qiujiang & Hu, Xiaosong, 2017. "Charging optimization in lithium-ion batteries based on temperature rise and charge time," Applied Energy, Elsevier, vol. 194(C), pages 569-577.
    2. Jiang, Jiuchun & Ruan, Haijun & Sun, Bingxiang & Zhang, Weige & Gao, Wenzhong & Wang, Le Yi & Zhang, Linjing, 2016. "A reduced low-temperature electro-thermal coupled model for lithium-ion batteries," Applied Energy, Elsevier, vol. 177(C), pages 804-816.
    3. Li, Xiaoyu & Fan, Guodong & Rizzoni, Giorgio & Canova, Marcello & Zhu, Chunbo & Wei, Guo, 2016. "A simplified multi-particle model for lithium ion batteries via a predictor-corrector strategy and quasi-linearization," Energy, Elsevier, vol. 116(P1), pages 154-169.
    4. Ansari, Amir Babak & Esfahanian, Vahid & Torabi, Farschad, 2016. "Discharge, rest and charge simulation of lead-acid batteries using an efficient reduced order model based on proper orthogonal decomposition," Applied Energy, Elsevier, vol. 173(C), pages 152-167.
    5. Truchot, Cyril & Dubarry, Matthieu & Liaw, Bor Yann, 2014. "State-of-charge estimation and uncertainty for lithium-ion battery strings," Applied Energy, Elsevier, vol. 119(C), pages 218-227.
    6. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
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