A dataset of over one thousand computed tomography scans of battery cells

• Published in: Data in brief Vol. 55; p. 110614
• Main Authors: Condon, Amariah, Buscarino, Bailey, Moch, Eric, Sehnert, William J., Miles, Owen, Herring, Patrick K., Attia, Peter M.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.08.2024; Elsevier
• Subjects: Battery quality; Battery variability; Computed tomography; Computer vision; Data; Lithium-ion battery; Manufacturing variation; Sodium-ion battery; Lithium-ion battery; Computer vision; Sodium-ion battery; Computed tomography; Battery variability; Manufacturing variation; Battery quality
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2024.110614

Battery technology is increasingly important for global electrification efforts. However, batteries are highly sensitive to small manufacturing variations that can induce reliability or safety issues. An important technology for battery quality control is computed tomography (CT) scanning, which is widely used for non-destructive 3D inspection across a variety of clinical and industrial applications. Historically, however, the utility of CT scanning for high-volume manufacturing has been limited by its low throughput as well as the difficulty of handling its large file sizes. In this work, we present a dataset of over one thousand CT scans of as-produced commercially available batteries. The dataset spans various chemistries (lithium-ion and sodium-ion) as well as various battery form factors (cylindrical, pouch, and prismatic). We evaluate seven different battery types in total. The manufacturing variability and the presence of battery defects can be observed via this dataset. This dataset may be of interest to scientists and engineers working on battery technology, computer vision, or both.