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

• Published in: Electrochemistry communications 2022-03, Vol.136, p.107224, Article 107224
• Main Authors: Yang, Yazheng, Li, Ning, Wang, Bin, Li, Na, Gao, Kai, Liang, Yudong, Wei, Yimin, Yang, Le, Song, Wei-Li, Chen, Haosen
• Format: Article
• Language: English
• Subjects: Deep learning; Microstructure evolution; Mini-cylindrical battery; Real structure; Thickness and porosity
• ISSN: 1388-2481; 1873-1902
• DOI: 10.1016/j.elecom.2022.107224

The microstructure evolution of electrodes with a mini-cylindrical battery was studied by deep learning combined with a cross-section polisher and scanning electron microscope. [Display omitted] •The evolution of electrode microstructure at different states of charge is studied, considering the influence of the battery structure.•A larger representative region of the electrode is obtained by a combination of scanning electron microscopy (SEM) and a cross-section polisher (CP).•A modified U-Net neural network approach is proposed to improve the accuracy of 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.