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

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 segmen

SUBMITTER: Muller S 

PROVIDER: S-EPMC8551326 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature