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Quantifying the factors limiting rate performance in battery electrodes

Author

Listed:
  • Ruiyuan Tian

    (Trinity College Dublin
    Trinity College Dublin)

  • Sang-Hoon Park

    (Trinity College Dublin
    Trinity College Dublin)

  • Paul J. King

    (Nokia)

  • Graeme Cunningham

    (Trinity College Dublin
    Trinity College Dublin)

  • João Coelho

    (Trinity College Dublin
    Trinity College Dublin)

  • Valeria Nicolosi

    (Trinity College Dublin
    Trinity College Dublin)

  • Jonathan N. Coleman

    (Trinity College Dublin
    Trinity College Dublin)

Abstract

One weakness of batteries is the rapid falloff in charge-storage capacity with increasing charge/discharge rate. Rate performance is related to the timescales associated with charge/ionic motion in both electrode and electrolyte. However, no general fittable model exists to link capacity-rate data to electrode/electrolyte properties. Here we demonstrate an equation which can fit capacity versus rate data, outputting three parameters which fully describe rate performance. Most important is the characteristic time associated with charge/discharge which can be linked by a second equation to physical electrode/electrolyte parameters via various rate-limiting processes. We fit these equations to ~200 data sets, deriving parameters such as diffusion coefficients or electrolyte conductivities. It is possible to show which rate-limiting processes are dominant in a given situation, facilitating rational design and cell optimisation. In addition, this model predicts the upper speed limit for lithium/sodium ion batteries, yielding a value that is consistent with the fastest electrodes in the literature.

Suggested Citation

  • Ruiyuan Tian & Sang-Hoon Park & Paul J. King & Graeme Cunningham & João Coelho & Valeria Nicolosi & Jonathan N. Coleman, 2019. "Quantifying the factors limiting rate performance in battery electrodes," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09792-9
    DOI: 10.1038/s41467-019-09792-9
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    Cited by:

    1. Xinchao Lu & Huachao Yang & Zheng Bo & Biyao Gong & Mengyu Cao & Xia Chen & Erka Wu & Jianhua Yan & Kefa Cen & Kostya (Ken) Ostrikov, 2022. "Aligned Ti 3 C 2 T X Aerogel with High Rate Performance, Power Density and Sub-Zero-Temperature Stability," Energies, MDPI, vol. 15(3), pages 1-12, February.
    2. 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).
    3. Khabibulla A. Abdullin & Maratbek T. Gabdullin & Zhanar K. Kalkozova & Shyryn T. Nurbolat & Mojtaba Mirzaeian, 2023. "Symmetrical Composite Supercapacitor Based on Activated Carbon and Cobalt Nanoparticles with High Cyclic Stability and Current Load," Energies, MDPI, vol. 16(11), pages 1-19, May.
    4. Dmitry Agafonov & Aleksandr Bobyl & Aleksandr Kamzin & Alexey Nashchekin & Evgeniy Ershenko & Arseniy Ushakov & Igor Kasatkin & Vladimir Levitskii & Mikhail Trenikhin & Evgeniy Terukov, 2023. "Phase-Homogeneous LiFePO 4 Powders with Crystallites Protected by Ferric-Graphite-Graphene Composite," Energies, MDPI, vol. 16(3), pages 1-28, February.
    5. Wu, Bin & Zhang, Buyi & Deng, Changyu & Lu, Wei, 2022. "Physics-encoded deep learning in identifying battery parameters without direct knowledge of ground truth," Applied Energy, Elsevier, vol. 321(C).
    6. Hatherall, Ollie & Barai, Anup & Niri, Mona Faraji & Wang, Zeyuan & Marco, James, 2024. "Novel battery power capability assessment for improved eVTOL aircraft landing," Applied Energy, Elsevier, vol. 361(C).
    7. 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).
    8. Cian Gabbett & Luke Doolan & Kevin Synnatschke & Laura Gambini & Emmet Coleman & Adam G. Kelly & Shixin Liu & Eoin Caffrey & Jose Munuera & Catriona Murphy & Stefano Sanvito & Lewys Jones & Jonathan N, 2024. "Quantitative analysis of printed nanostructured networks using high-resolution 3D FIB-SEM nanotomography," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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