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

Simon Müller, Christina Sauter, Ramesh Shunmugasundaram, Nils Wenzler, Vincent Andrade, Francesco Carlo, Ender Konukoglu and Vanessa Wood ()
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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

Nature Communications, 2021, vol. 12, issue 1, 1-12

Abstract: 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.

Date: 2021
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DOI: 10.1038/s41467-021-26480-9

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