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.