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A 3D distributed circuit-electrochemical model for the inner inhomogeneity of lithium-ion battery

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  • Liu, Yang
  • Zhang, Caiping
  • Jiang, Jiuchun
  • Zhang, Linjing
  • Zhang, Weige
  • Lao, Li
  • Yang, Shichun

Abstract

Performance degradation and operational safety are vital issues for lithium-ion batteries. After summarizing each battery failure accident, it is found that local defects caused by the inconsistency within the battery are one of the critical reasons for battery failure. To study the influence of the internal inconsistency of the battery on its safety performance, a 3D distributed circuit and electrochemical coupling model that can reflect the internal structure of the battery are established. Moreover, the model’s solid-phase potential and liquid-phase lithium ion concentration are corrected to improve the simulation accuracy. The RMSE of the model is less than 10 mV under 1.0 C constant current charging condition and dynamic discharge condition. Based on the proposed distributed model, this paper investigates the influence of the cell size, tab position, and other structural parameters on the internal inconsistency of the cell. And the phenomenon of “current reversal” and the possible local lithium deposition away from the tab position is discovered. The model’s correctness and efficacy in simulating internal inconsistencies are further illustrated by reproducing the local lithium deposition of a homemade LFP cell and a commercial NCM cell by controlling the charging current.

Suggested Citation

  • Liu, Yang & Zhang, Caiping & Jiang, Jiuchun & Zhang, Linjing & Zhang, Weige & Lao, Li & Yang, Shichun, 2023. "A 3D distributed circuit-electrochemical model for the inner inhomogeneity of lithium-ion battery," Applied Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922016476
    DOI: 10.1016/j.apenergy.2022.120390
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    References listed on IDEAS

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    1. Wang, Shunli & Takyi-Aninakwa, Paul & Jin, Siyu & Yu, Chunmei & Fernandez, Carlos & Stroe, Daniel-Ioan, 2022. "An improved feedforward-long short-term memory modeling method for the whole-life-cycle state of charge prediction of lithium-ion batteries considering current-voltage-temperature variation," Energy, Elsevier, vol. 254(PA).
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    1. Rodríguez-Iturriaga, Pablo & García, Víctor Manuel & Rodríguez-Bolívar, Salvador & Valdés, Enrique Ernesto & Anseán, David & López-Villanueva, Juan Antonio, 2024. "A coupled electrothermal lithium-ion battery reduced-order model including heat generation due to solid diffusion," Applied Energy, Elsevier, vol. 367(C).
    2. Zhang, Chengzhong & Zhao, Hongyu & Wang, Liye & Liao, Chenglin & Wang, Lifang, 2024. "A comparative study on state-of-charge estimation for lithium-rich manganese-based battery based on Bayesian filtering and machine learning methods," Energy, Elsevier, vol. 306(C).
    3. Rodríguez-Iturriaga, Pablo & Anseán, David & Rodríguez-Bolívar, Salvador & García, Víctor Manuel & González, Manuela & López-Villanueva, Juan Antonio, 2024. "Modeling current-rate effects in lithium-ion batteries based on a distributed, multi-particle equivalent circuit model," Applied Energy, Elsevier, vol. 353(PA).

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