Advancing Vanadium Redox Flow Battery Analysis: A Deep Learning Approach for High-Throughput 3D Visualization and Bubble Quantification

Abstract

This work harnesses deep learning to expedite vanadium redox flow battery research data analyses. Recent studies have highlighted the significance of analyzing bubbles within vanadium redox flow batteries. The elucidation of these bubbles had remained elusive in direct imaging until advancements in cell design facilitated their observation through synchrotron X-ray tomography. Yet, the considerable volume of slices per tomograph and the complexity of the features present challenges for analyzing bubbles. To tackle this issue, we propose a deep learning-based framework that allows experimentalists to conduct high-throughput analyses based on synchrotron X-ray tomographic images of vanadium redox flow batteries. We conducted a benchmarking study on various U-Net configurations using a dataset that includes three complete volumes. These volumes represent different cell configurations and encompass 2294 annotated images. Through a multi-class semantic segmentation approach, we aimed to identify four distinct classes, such as bubbles, electrolytes, membranes, and gaskets. The optimal model achieved a precision of 98%, a recall of 97%, and an F1-score of 97% on the validation set. Following segmentation, the framework facilitates rapid differentiation of electrodes, quantification of bubble volume, individual bubble shape analysis, generation of 2D bubble density maps, and calculation of membrane blockage. All results are readily accessible for interactive, on-site visualization within a 3D environment. The publicly available software allows users to engage with the data in a comprehensive and intuitive manner. For access, visit the following GitHub repository: https://github.com/andyco98/UTILE-Redox.