Detection of cathode agglomerates in batteries applying laser infrared thermography

The key to enhancing the cost-effectiveness of the lithium-ion production process is reducing the scrap rate of cells earlier in production. Defective or under-performing cells are detected during the formation or aging steps. The performance of these cells is influenced by earlier production stages, with the coating and calendering processes being crucial steps where quality control is most important. This motivates the use of inline measurement techniques at this stage. Laser thermography is a powerful measurement method that can detect surface and subsurface defects or geometric deviations. This study focuses on the use of a relatively new non-destructive testing technique, named laser infrared thermography (L-IRT), to identify defect types aggregated in cathode electrode sheets. The object movement is simulated using a rotating roll. The data is reconstructed and evaluated using common active thermography evaluation methods, such as the gradient method, thermal signal reconstruction, and pulse phase thermography. The results were analyzed both qualitatively and quantitatively using the signal-to-noise ratio and contrast-to-noise ratio. Here the gradient method showed the highest metrics. Further, in a non-calendered electrode sheet a distinct pattern was made visible using laser infrared thermography, which allows in the future optimisations of the slot die based on laser infrared thermography measurements.