X-ray computed tomography (CT) systems have become indispensable tools for non-destructive 3D imaging of battery cells, enabling detailed internal inspection without disassembly. These systems are particularly valuable for identifying defects such as electrode misalignment, voids, cracks, and dendrite formation in prismatic and pouch cells. Unlike destructive methods that require cell opening, X-ray CT preserves sample integrity while providing volumetric data for comprehensive analysis.
The principle of X-ray CT involves capturing multiple 2D radiographic projections of a battery cell from different angles as it rotates. These projections are then reconstructed into a 3D volume using specialized algorithms. The quality of the reconstructed image depends on several factors, including resolution, contrast, and the reconstruction software’s capabilities.
Spatial resolution is critical for detecting small-scale defects. Industrial X-ray CT systems typically achieve resolutions in the range of 1 to 50 micrometers, depending on the system’s configuration and the sample size. For battery applications, a resolution of 5 to 20 micrometers is often sufficient to identify electrode misalignment, separator integrity issues, and void formations. Higher resolutions, below 5 micrometers, may be necessary for observing dendrite growth or fine cracks, but these require longer scan times and more powerful X-ray sources.
Contrast mechanisms in X-ray CT rely on differences in material density and atomic number. Battery components such as lithium metal, aluminum, copper, and polymer separators exhibit varying X-ray attenuation, allowing differentiation in the reconstructed images. However, low-density materials like lithium can be challenging to image due to weak attenuation. Phase-contrast imaging techniques may be employed to enhance visibility in such cases.
Reconstruction software plays a crucial role in converting raw projection data into a usable 3D volume. Algorithms such as filtered back projection (FBP) or iterative reconstruction methods are commonly used. Advanced software may include artifact correction for beam hardening, scatter, or ring artifacts, which can degrade image quality. Post-processing tools enable segmentation of different components, quantitative analysis of defect sizes, and volumetric measurements.
Compared to 2D X-ray imaging, X-ray CT provides significant advantages. Traditional 2D radiography only offers a single projection, making it difficult to discern overlapping features or locate defects in the depth dimension. For example, electrode misalignment in a prismatic cell may appear as a blurred edge in 2D but can be precisely measured in 3D. Similarly, dendrites penetrating a separator are more easily visualized in CT slices rather than a single X-ray image where they may be obscured by other structures.
A key application of X-ray CT is detecting manufacturing defects. Electrode misalignment, where the anode and cathode are not perfectly superimposed, can lead to capacity loss or internal short circuits. CT imaging allows measurement of the overlap margin and identification of regions where misalignment exceeds tolerances. Voids or delaminations within electrodes, often caused by slurry coating inconsistencies or calendering issues, are also readily visible in 3D scans. These defects can impede ion transport and increase local current densities, accelerating degradation.
Dendrite formation, a critical safety concern, can be monitored using X-ray CT. Lithium dendrites are thin, needle-like structures that grow through the electrolyte and separator, potentially causing internal shorts. While detecting early-stage dendrites requires high resolution, CT can track their progression over time, providing insights into failure mechanisms.
Thermal and mechanical stress-induced defects are another area where CT excels. Cycling-induced cracks in electrodes or separators can be mapped in 3D, revealing propagation patterns that correlate with performance decay. Similarly, swelling in pouch cells due to gas generation can be quantified by comparing pre- and post-aging scans.
Despite its advantages, X-ray CT has limitations. Scan times can range from minutes to hours, depending on resolution and sample size, making it less suitable for high-throughput inspection. Additionally, the equipment cost and computational requirements for data processing may be prohibitive for some applications.
In summary, X-ray CT systems provide unparalleled insights into the internal structure of battery cells, enabling non-destructive detection of defects critical to performance and safety. While 2D X-ray methods offer faster inspection, they lack the depth information necessary for comprehensive analysis. As battery technologies advance, the role of X-ray CT in quality control and failure analysis will continue to grow, supported by improvements in resolution, contrast, and reconstruction algorithms.
For prismatic and pouch cells, where internal geometry and layer alignment are crucial, X-ray CT is an essential tool for manufacturers and researchers alike. Its ability to visualize hidden defects without sample destruction makes it invaluable for both development and production quality assurance. Future advancements may focus on faster scanning techniques and AI-assisted defect recognition to further enhance its utility in the battery industry.