IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v270y2020ics0306261920306218.html
   My bibliography  Save this article

Improvement of electrochemical homogeneity for lithium-ion batteries enabled by a conjoined-electrode structure

Author

Listed:
  • Xiong, Ruoyu
  • Zhang, Tengfang
  • Huang, Tianlun
  • Li, Maoyuan
  • Zhang, Yun
  • Zhou, Huamin

Abstract

Electrochemical inhomogeneity of lithium-ion batteries stemming from heterogeneous electrode microstructure adversely affects battery rate-performance, lifetime and safety. It is attributed to manufacturing errors of electrodes in previous studies. However, the significant heterogeneous electrochemistry is still found in commercial battery electrodes with high manufacturing accuracy. Here, we propose a conjoined-electrode structure to improve the electrochemical homogeneity, in which every two adjacent cathodes or anodes are connected through microholes on current collectors. The commercial level pouch lithium-ion battery with the conjoined-electrode structure is fabricated and it displays a better rate capability (26% higher capacity at 3C rate) and a lower capacity degradation rate (decreased by 50% in the cycling tests at 1C rate). A 3-D electrochemical-thermal model is used in simulation with inhomogeneous situations to reveal the self-balancing effects of state of charge, current density, and Li-ion concentration in the conjoined-electrode structure, which facilitate more homogeneous electrochemistry in lithium-ion batteries. The limitation factor of the self-balancing effects varies depending on the structural parameters, which limits the conjoined-electrode structure design.

Suggested Citation

  • Xiong, Ruoyu & Zhang, Tengfang & Huang, Tianlun & Li, Maoyuan & Zhang, Yun & Zhou, Huamin, 2020. "Improvement of electrochemical homogeneity for lithium-ion batteries enabled by a conjoined-electrode structure," Applied Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:appene:v:270:y:2020:i:c:s0306261920306218
    DOI: 10.1016/j.apenergy.2020.115109
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920306218
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.115109?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. Li, Junqiu & Sun, Danni & Jin, Xin & Shi, Wentong & Sun, Chao, 2019. "Lithium-ion battery overcharging thermal characteristics analysis and an impedance-based electro-thermal coupled model simulation," Applied Energy, Elsevier, vol. 254(C).
    2. Zhang, Caiping & Jiang, Yan & Jiang, Jiuchun & Cheng, Gong & Diao, Weiping & Zhang, Weige, 2017. "Study on battery pack consistency evolutions and equilibrium diagnosis for serial- connected lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 510-519.
    3. Yayuan Liu & Yangying Zhu & Yi Cui, 2019. "Challenges and opportunities towards fast-charging battery materials," Nature Energy, Nature, vol. 4(7), pages 540-550, July.
    4. Du, Jiuyu & Liu, Ye & Mo, Xinying & Li, Yalun & Li, Jianqiu & Wu, Xiaogang & Ouyang, Minggao, 2019. "Impact of high-power charging on the durability and safety of lithium batteries used in long-range battery electric vehicles," Applied Energy, Elsevier, vol. 255(C).
    5. Ren, Dongsheng & Feng, Xuning & Lu, Languang & He, Xiangming & Ouyang, Minggao, 2019. "Overcharge behaviors and failure mechanism of lithium-ion batteries under different test conditions," Applied Energy, Elsevier, vol. 250(C), pages 323-332.
    6. Ouyang, Minggao & Feng, Xuning & Han, Xuebing & Lu, Languang & Li, Zhe & He, Xiangming, 2016. "A dynamic capacity degradation model and its applications considering varying load for a large format Li-ion battery," Applied Energy, Elsevier, vol. 165(C), pages 48-59.
    7. Arno Kwade & Wolfgang Haselrieder & Ruben Leithoff & Armin Modlinger & Franz Dietrich & Klaus Droeder, 2018. "Current status and challenges for automotive battery production technologies," Nature Energy, Nature, vol. 3(4), pages 290-300, April.
    8. Jiang, Z.Y. & Qu, Z.G. & Zhou, L. & Tao, W.Q., 2017. "A microscopic investigation of ion and electron transport in lithium-ion battery porous electrodes using the lattice Boltzmann method," Applied Energy, Elsevier, vol. 194(C), pages 530-539.
    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. He, Tengfei & Zhang, Teng & Wang, Zhirong & Cai, Qiong, 2022. "A comprehensive numerical study on electrochemical-thermal models of a cylindrical lithium-ion battery during discharge process," Applied Energy, Elsevier, vol. 313(C).
    2. Chen, Haosen & Fan, Jinbao & Zhang, Mingliang & Feng, Xiaolong & Zhong, Ximing & He, Jianchao & Ai, Shigang, 2023. "Mechanism of inhomogeneous deformation and equal-stiffness design of large-format prismatic lithium-ion batteries," Applied Energy, Elsevier, vol. 332(C).
    3. Zhang, Jiarui & Wang, Chao & Li, Jinzhong & Xie, Yuguang & Mao, Lei & Hu, Zhiyong, 2023. "A Bayesian method for capacity degradation prediction of lithium-ion battery considering both within and cross group heterogeneity," Applied Energy, Elsevier, vol. 351(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. Qin, Yudi & Du, Jiuyu & Lu, Languang & Gao, Ming & Haase, Frank & Li, Jianqiu & Ouyang, Minggao, 2020. "A rapid lithium-ion battery heating method based on bidirectional pulsed current: Heating effect and impact on battery life," Applied Energy, Elsevier, vol. 280(C).
    2. Jiang, Z.Y. & Qu, Z.G., 2019. "Lithium–ion battery thermal management using heat pipe and phase change material during discharge–charge cycle: A comprehensive numerical study," Applied Energy, Elsevier, vol. 242(C), pages 378-392.
    3. Lin, Xiang-Wei & Li, Yu-Bai & Wu, Wei-Tao & Zhou, Zhi-Fu & Chen, Bin, 2024. "Advances on two-phase heat transfer for lithium-ion battery thermal management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. Tian, Jiaqiang & Fan, Yuan & Pan, Tianhong & Zhang, Xu & Yin, Jianning & Zhang, Qingping, 2024. "A critical review on inconsistency mechanism, evaluation methods and improvement measures for lithium-ion battery energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    5. Jiong Yang & Fanyong Cheng & Maxwell Duodu & Miao Li & Chao Han, 2022. "High-Precision Fault Detection for Electric Vehicle Battery System Based on Bayesian Optimization SVDD," Energies, MDPI, vol. 15(22), pages 1-20, November.
    6. Ashleigh Townsend & Rupert Gouws, 2022. "A Comparative Review of Lead-Acid, Lithium-Ion and Ultra-Capacitor Technologies and Their Degradation Mechanisms," Energies, MDPI, vol. 15(13), pages 1-29, July.
    7. He, Tengfei & Zhang, Teng & Wang, Zhirong & Cai, Qiong, 2022. "A comprehensive numerical study on electrochemical-thermal models of a cylindrical lithium-ion battery during discharge process," Applied Energy, Elsevier, vol. 313(C).
    8. Chang, Chun & Wu, Yutong & Jiang, Jiuchun & Jiang, Yan & Tian, Aina & Li, Taiyu & Gao, Yang, 2022. "Prognostics of the state of health for lithium-ion battery packs in energy storage applications," Energy, Elsevier, vol. 239(PB).
    9. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    10. Abdollahifar, M. & Molaiyan, P. & Lassi, U. & Wu, N.L. & Kwade, A., 2022. "Multifunctional behaviour of graphite in lithium–sulfur batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    11. Yuqiang Zeng & Buyi Zhang & Yanbao Fu & Fengyu Shen & Qiye Zheng & Divya Chalise & Ruijiao Miao & Sumanjeet Kaur & Sean D. Lubner & Michael C. Tucker & Vincent Battaglia & Chris Dames & Ravi S. Prashe, 2023. "Extreme fast charging of commercial Li-ion batteries via combined thermal switching and self-heating approaches," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    12. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    13. Entwistle, Jake & Ge, Ruihuan & Pardikar, Kunal & Smith, Rachel & Cumming, Denis, 2022. "Carbon binder domain networks and electrical conductivity in lithium-ion battery electrodes: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    14. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    15. Li, Xiaoyu & Zhang, Zuguang & Wang, Wenhui & Tian, Yong & Li, Dong & Tian, Jindong, 2020. "Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis," Applied Energy, Elsevier, vol. 262(C).
    16. He, Xitian & Sun, Bingxiang & Zhang, Weige & Su, Xiaojia & Ma, Shichang & Li, Hao & Ruan, Haijun, 2023. "Inconsistency modeling of lithium-ion battery pack based on variational auto-encoder considering multi-parameter correlation," Energy, Elsevier, vol. 277(C).
    17. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
    18. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    19. Xiong, Rui & Pan, Yue & Shen, Weixiang & Li, Hailong & Sun, Fengchun, 2020. "Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    20. Bandara, T.G. Thusitha Asela & Viera, J.C. & González, M., 2022. "The next generation of fast charging methods for Lithium-ion batteries: The natural current-absorption methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).

    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:270:y:2020:i:c:s0306261920306218. 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.