The quality assurance of Lithium-ion batteries using microscopy is of great importance, as they are widely used in various applications like consumer electronics, electric vehicles, and energy storage systems. Ensuring their quality is crucial to prevent performance problems, safety hazards and premature aging. Microscopy provides a detailed examination of the microstructure of the battery, allowing for the identification and characterisation of defects such as cracks, delaminations, foreign particles, or deformations. By detecting and addressing these defects as a part of an at-line quality assurance process, manufacturers can enhance battery performance, prolong lifespan, and mitigate safety risks. Furthermore, microscopy enables researchers to investigate the root causes of defects, optimise manufacturing processes, and support the development of new materials and designs for next-generation batteries.
By leveraging automated and accurate defect and anomaly detection, deep learning contributes to improved battery performance and safety. This work provides an overview of deep learning techniques for defect detection in microstructural images of Lithium-ion batteries obtained through light-optical microscopy, computer tomography, and X-ray microscopy. Supervised and unsupervised approaches like object detection, image reconstruction and clustering of deep features and their application on the microscopy data are presented.