IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v331y2023ics0306261922016476.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922016476
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120390?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shi, Haotian & Wang, Shunli & Huang, Qi & Fernandez, Carlos & Liang, Jianhong & Zhang, Mengyun & Qi, Chuangshi & Wang, Liping, 2024. "Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries," Applied Energy, Elsevier, vol. 353(PB).
    2. Peng Song & Zhisheng Zhang, 2023. "Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM," Energies, MDPI, vol. 16(9), pages 1-13, April.
    3. Choon Kit Chan & Chi Hong Chung & Jeyagopi Raman, 2023. "Optimizing Thermal Management System in Electric Vehicle Battery Packs for Sustainable Transportation," Sustainability, MDPI, vol. 15(15), pages 1-14, August.
    4. Ta, Yuntian & Li, Yanfeng & Cai, Wenan & Zhang, Qianqian & Wang, Zhijian & Dong, Lei & Du, Wenhua, 2023. "Adaptive staged remaining useful life prediction method based on multi-sensor and multi-feature fusion," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    5. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiaoyong & Fernandez, Carlos, 2022. "An optimized long short-term memory-weighted fading extended Kalman filtering model with wide temperature adaptation for the state of charge estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 326(C).
    6. He, Qijiao & Li, Zheng & Zhao, Dongqi & Yu, Jie & Tan, Peng & Guo, Meiting & Liao, Tianjun & Zhao, Tianshou & Ni, Meng, 2023. "A 3D modelling study on all vanadium redox flow battery at various operating temperatures," Energy, Elsevier, vol. 282(C).
    7. Wei, Yupeng & Wu, Dazhong, 2023. "Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    8. Gu, Xinyu & See, K.W. & Li, Penghua & Shan, Kangheng & Wang, Yunpeng & Zhao, Liang & Lim, Kai Chin & Zhang, Neng, 2023. "A novel state-of-health estimation for the lithium-ion battery using a convolutional neural network and transformer model," Energy, Elsevier, vol. 262(PB).
    9. Fan, Tian-E & Liu, Song-Ming & Yang, Hao & Li, Peng-Hua & Qu, Baihua, 2023. "A fast active balancing strategy based on model predictive control for lithium-ion battery packs," Energy, Elsevier, vol. 279(C).
    10. Yang, Bowen & Wang, Dafang & Sun, Xu & Chen, Shiqin & Wang, Xingcheng, 2023. "Offline order recognition for state estimation of Lithium-ion battery using fractional order model," Applied Energy, Elsevier, vol. 341(C).
    11. Zhang, Xiang & Liu, Peng & Lin, Ni & Zhang, Zhaosheng & Wang, Zhenpo, 2023. "A novel battery abnormality detection method using interpretable Autoencoder," Applied Energy, Elsevier, vol. 330(PB).
    12. Mayer, Martin János & Biró, Bence & Szücs, Botond & Aszódi, Attila, 2023. "Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning," Applied Energy, Elsevier, vol. 336(C).
    13. Li, Jizi & Liu, Fangbing & Zhang, Justin Z. & Tong, Zeping, 2023. "Optimal configuration of electric vehicle battery recycling system under across-network cooperation," Applied Energy, Elsevier, vol. 338(C).
    14. Couto, Luis. D. & Charkhgard, Mohammad & Karaman, Berke & Job, Nathalie & Kinnaert, Michel, 2023. "Lithium-ion battery design optimization based on a dimensionless reduced-order electrochemical model," Energy, Elsevier, vol. 263(PE).
    15. Li, Yuanmao & Liu, Guixiong & Deng, Wei & Li, Zuyu, 2024. "Comparative study on parameter identification of an electrochemical model for lithium-ion batteries via meta-heuristic methods," Applied Energy, Elsevier, vol. 367(C).
    16. Xu, Xiaodong & Tang, Shengjin & Han, Xuebing & Lu, Languang & Wu, Yu & Yu, Chuanqiang & Sun, Xiaoyan & Xie, Jian & Feng, Xuning & Ouyang, Minggao, 2023. "Fast capacity prediction of lithium-ion batteries using aging mechanism-informed bidirectional long short-term memory network," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    17. Carlo Villante, 2023. "A Novel SW Tool for the Evaluation of Expected Benefits of V2H Charging Devices Utilization in V2B Building Contexts," Energies, MDPI, vol. 16(7), pages 1-25, March.
    18. Xue, Jingsong & Ma, Wentao & Feng, Xiaoyang & Guo, Peng & Guo, Yaosong & Hu, Xianzhi & Chen, Badong, 2023. "Stacking integrated learning model via ELM and GRU with mixture correntropy loss for robust state of health estimation of lithium-ion batteries," Energy, Elsevier, vol. 284(C).
    19. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Li, Huan & Xu, Wenhua & Fernandez, Carlos, 2022. "An optimized relevant long short-term memory-squared gain extended Kalman filter for the state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 260(C).
    20. Zhang, Chen & Wang, Hongmin & Wu, Lifeng, 2023. "Life prediction model for lithium-ion battery considering fast-charging protocol," Energy, Elsevier, vol. 263(PE).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922016476. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.