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3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling

Author

Listed:
  • Xuekun Lu

    (University College London
    National Physical Laboratory
    The Faraday Institution)

  • Antonio Bertei

    (University of Pisa)

  • Donal P. Finegan

    (National Renewable Energy Laboratory)

  • Chun Tan

    (University College London
    The Faraday Institution)

  • Sohrab R. Daemi

    (University College London)

  • Julia S. Weaving

    (University College London)

  • Kieran B. O’Regan

    (The Faraday Institution
    University of Birmingham)

  • Thomas M. M. Heenan

    (University College London
    The Faraday Institution)

  • Gareth Hinds

    (National Physical Laboratory)

  • Emma Kendrick

    (The Faraday Institution
    University of Birmingham)

  • Dan J. L. Brett

    (University College London
    The Faraday Institution)

  • Paul R. Shearing

    (University College London
    The Faraday Institution)

Abstract

Driving range and fast charge capability of electric vehicles are heavily dependent on the 3D microstructure of lithium-ion batteries (LiBs) and substantial fundamental research is required to optimise electrode design for specific operating conditions. Here we have developed a full microstructure-resolved 3D model using a novel X-ray nano-computed tomography (CT) dual-scan superimposition technique that captures features of the carbon-binder domain. This elucidates how LiB performance is markedly affected by microstructural heterogeneities, particularly under high rate conditions. The elongated shape and wide size distribution of the active particles not only affect the lithium-ion transport but also lead to a heterogeneous current distribution and non-uniform lithiation between particles and along the through-thickness direction. Building on these insights, we propose and compare potential graded-microstructure designs for next-generation battery electrodes. To guide manufacturing of electrode architectures, in-situ X-ray CT is shown to reliably reveal the porosity and tortuosity changes with incremental calendering steps.

Suggested Citation

  • Xuekun Lu & Antonio Bertei & Donal P. Finegan & Chun Tan & Sohrab R. Daemi & Julia S. Weaving & Kieran B. O’Regan & Thomas M. M. Heenan & Gareth Hinds & Emma Kendrick & Dan J. L. Brett & Paul R. Shear, 2020. "3D microstructure design of lithium-ion battery electrodes assisted by X-ray nano-computed tomography and modelling," Nature Communications, Nature, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-15811-x
    DOI: 10.1038/s41467-020-15811-x
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    Cited by:

    1. Ying Da Wang & Quentin Meyer & Kunning Tang & James E. McClure & Robin T. White & Stephen T. Kelly & Matthew M. Crawford & Francesco Iacoviello & Dan J. L. Brett & Paul R. Shearing & Peyman Mostaghimi, 2023. "Large-scale physically accurate modelling of real proton exchange membrane fuel cell with deep learning," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    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. 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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