A Dataset for Visual Classification of Bulging Pouch Battery Cells

The rapid advancement of lithium-ion battery technologies, particularly pouch cells, has driven significant growth in electric vehicles, mobile devices, and renewable energy storage. However, pouch cells are especially susceptible to mechanical deformation and failure, including bulging caused by internal gas formation—a common indicator of cell aging or imminent failure. In this study, we developed a visual dataset of bulging pouch battery cells to support real-time diagnostics and safety monitoring in industrial and laboratory environments. The dataset includes 200 high-resolution images (100 bulged, 100 normal) curated through a web-crawling and filtering pipeline. The dataset is benchmarked across several traditional machine learning models to evaluate performance and feasibility for edge AI deployment. The best model achieved strong classification accuracy while maintaining a small computational footprint suitable for embedded applications.