Microstructure evolution of lithium-ion battery electrodes at different states of charge: Deep learning-based segmentation

The evolution of the microstructure of a battery electrode is closely related to battery performance. Characterization and visualization of the evolution of the microstructure is essential for optimization of manufactured electrodes. The validity of the battery structure representation affects the accuracy of the extracted microstructure parameters. In this study, a mini-cylindrical battery is designed to allow microstructure parameters to be obtained at different states of charge, bearing in mind the influence of the real battery structure. An argon-ion cross-section polisher is used to obtain a large area of the electrode for observation. In addition, an image segmentation method based on a modified U-Net neural network is developed to enhance the quality of the extracted microstructure. The relationship between porosity and thickness at different states of electrode charge is presented through experiments and deep learning of images. This method provides new insight into the evolution of electrode microstructure and can potentially guide the manufacturing of lithium-ion batteries.