The high demand for all kinds of Electrical Vehicles (EV) increases the pressure on battery cell manufactures – they have to increase the volume of all types of battery cells, they have to lower the costs and all that by keeping their quality high or even improve their yield. The squaring of this circle is not always working, which reflects in quite high scrap rates at the battery factories or in expensive recalls of already delivered EVs. The root causes of battery related failures are often found in the production process and can be mitigated by consequent usage of industrial X-ray Computed Tomography (CT).
This poster gives an overview of the inspection method CT and shows, how typical failures in different types of Li-Ion battery cells look like. Slice images from real CT data sets of prismatic, cylindrical and pouch cells are used to visualize these failures. As CT is creating 3D data sets with the real geometry of the sample, the results are easy to interpret. They can also be used to do measurements of the sample. Therefor new software tools are shown, which help to catch the failures early and thus limit the impact of the failures. These innovative tools use Machine Learning (ML) to allow an Automated Defect Recognition (ADR). If the CT data are acquired as part of the mass production, archiving them in a data pool creates the source for the development of new analytics on new failure modes.
If CT is consequently used in the production process, we expect a reduction of the scrap rate by a minimum of 5% points, which will have a big impact on the productivity and economics of a battery factory and subsequently on the EV industry.
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