Image Enhancement and Feature Extraction for Battery Electrodes with Deep Learning

In the field of battery material science, the use of advanced imaging techniques is paramount for a comprehensive understanding of electrode materials. Computed Tomography (CT) has become an invaluable tool, offering insights into the internal structures of battery components. However, the challenges of low-contrast materials and small feature sizes inherent to battery electrodes necessitate innovative approaches for image enhancement and feature extraction. In this study, we explore the application of a machine learning approach, i.e., UNets, for enhancing CT images and employ a Variational Autoencoder (VAE) for feature extraction and clustering.

Our focus centers on a graphite-silicon composite electrode, chosen due to its relevance in battery materials and the complex imaging challenges it presents. Notably, the smallest features, such as carbon black and chipped graphite flakes, exist at nanometer scales, contributing to a heterogeneous structure. Moreover, the materials, including graphite and carbon black binder, share similarities in their core charge numbers, leading to low signal contrast, making their distinction challenging.

The study primarily addresses three key aspects:
Image Enhancement: We utilize UNets to enhance CT images of the graphite-silicon composite electrode. These neural networks excel in denoising and super-resolution, improving the visibility of small features and enhancing the overall image quality. The application of UNets leads to clearer, more informative images, aiding researchers in the analysis of battery materials in a faster way.
Feature Extraction: Through the implementation of a VAE, we perform feature extraction and clustering on the CT images. This technique enables the identification and isolation of important structural features within the electrode, offering insights into the material's composition and distribution. The extracted features can be instrumental in understanding the electrode's behavior during battery aging.
Applications: We discuss the practical applications of enhanced CT images and feature extraction in battery material science. The enriched images facilitate the accurate characterization of small-scale structures and the spatial distribution of materials within the electrode. This information can be used in the development of battery materials and manufacturing processes, contributing to enhanced performance, safety, and longevity.

Our study underscores the potential of utilizing cutting-edge image enhancement techniques and feature extraction methods in battery material science. By enhancing the quality of CT images and extracting relevant features, researchers can gain deeper insights into the intricate structures of battery electrodes. This research bridges the gap between imaging technology and materials science, offering valuable tools for advancing the development and manufacturing of high-performance batteries.