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Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes

Author

Listed:
  • Simon Müller

    (Department of Information Technology and Electrical Engineering, ETH Zurich)

  • Christina Sauter

    (Department of Information Technology and Electrical Engineering, ETH Zurich)

  • Ramesh Shunmugasundaram

    (Department of Information Technology and Electrical Engineering, ETH Zurich)

  • Nils Wenzler

    (Department of Information Technology and Electrical Engineering, ETH Zurich)

  • Vincent Andrade

    (Advanced Photon Source, Argonne National Laboratory)

  • Francesco Carlo

    (Advanced Photon Source, Argonne National Laboratory)

  • Ender Konukoglu

    (Department of Information Technology and Electrical Engineering, ETH Zurich)

  • Vanessa Wood

    (Department of Information Technology and Electrical Engineering, ETH Zurich)

Abstract

Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.

Suggested Citation

  • Simon Müller & Christina Sauter & Ramesh Shunmugasundaram & Nils Wenzler & Vincent Andrade & Francesco Carlo & Ender Konukoglu & Vanessa Wood, 2021. "Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26480-9
    DOI: 10.1038/s41467-021-26480-9
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    References listed on IDEAS

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    1. Martin Dierolf & Andreas Menzel & Pierre Thibault & Philipp Schneider & Cameron M. Kewish & Roger Wepf & Oliver Bunk & Franz Pfeiffer, 2010. "Ptychographic X-ray computed tomography at the nanoscale," Nature, Nature, vol. 467(7314), pages 436-439, September.
    2. Patrick Pietsch & Daniel Westhoff & Julian Feinauer & Jens Eller & Federica Marone & Marco Stampanoni & Volker Schmidt & Vanessa Wood, 2016. "Quantifying microstructural dynamics and electrochemical activity of graphite and silicon-graphite lithium ion battery anodes," Nature Communications, Nature, vol. 7(1), pages 1-11, December.
    3. Zhisen Jiang & Jizhou Li & Yang Yang & Linqin Mu & Chenxi Wei & Xiqian Yu & Piero Pianetta & Kejie Zhao & Peter Cloetens & Feng Lin & Yijin Liu, 2020. "Machine-learning-revealed statistics of the particle-carbon/binder detachment in lithium-ion battery cathodes," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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    Cited by:

    1. 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).

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