Image processing methods and light optical microscopy for in-situ quantification of chromatic change and anode dilation in Li-ion battery graphite anodes during (de-)lithiation

Abstract

In Lithium-ion batteries, the graphite anode is known to undergo a noticeable chromatic change during lithiation and de-lithiation by forming graphite intercalation compounds. Additionally, the graphite anode primarily contributes to the volume change of the battery. Using a novel in-situ optical microscopy setup for imaging cross-sections of Li-ion full cells, both effects can be studied simultaneously during charging and discharging. In this work, we describe feature extraction methods to quantify these effects in the image data (3730 images in total) captured during the lithiation and de-lithiation process. Automated and manual evaluations are compared. The images show graphite anodes and NMC 622 cathodes. For colorfulness, we evaluate different methods based on classical image processing. The metrics calculated with these approaches are compared to the results of ColorNet, which is a trainable colorfulness estimator based on deep convolutional neural networks. We propose a supervised semantic segmentation approach using U-Net for the layer thickness measurement and the anode dilation derived from it.