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.
Highlights
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 improving battery performance
We create the corresponding tomographic reconstructions of these artificially generated electrodes, incorporating the effects of the beamline and measurement. These tomographic simulations and the corresponding synthetic datasets form the input-output data pairs that can be used to train the network together with the real datasets. With this approach of combining real and synthetic training data, we significantly improve the accuracy of the segmentation and labeling of the x-ray tomographic microscopy (XTM) data into pore space, graphite and silicon particles, and the carbon black-binder domain
We image in total twelve samples, three from each of the cycling conditions, because a sample size of 70 μm is close to the limit of representativeness of electrode structure, with even commercial electrodes exhibiting heterogeneity at this length scale[5]
Summary
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 improving battery 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 apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation. Obtaining 3D reconstructions that can be accurately segmented and quantitatively analyzed is still a challenge, primarily due to (i) the diverging length scales present in LIB electrodes, (ii) the low contrast between key components, and (iii) the low attenuation of carbon-based materials[16] Active materials such as Li(Ni,Mn,Co)O2 have particle dimensions in the range of 1–10 μm[17] and contain transition metal elements that provide good contrast during absorption-based imaging and a reliable identification of the particles. High-resolution imaging typically requires long imaging times, often making it prohibitive to obtain statistically relevant data on the electrode scale by imaging many small samples serially[22,23]
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