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Inline monitoring of battery electrode lamination processes based on acoustic measurements

: Leithoff, Ruben; Dilger, Nikolas; Duckhorn, Frank; Blume, Stefan; Lembcke, Dario; Tschöpe, Constanze; Herrmann, Christoph; Dröder, Klaus

Fulltext ()

Batteries 7 (2021), No.1, Art. 19, 21 pp.
ISSN: 2313-0105
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
03ETE017A; DaLion 4.0
Fraunhofer-Gesellschaft FhG
11-76251-99-2/17; BattLTech
Journal Article, Electronic Publication
Fraunhofer IST ()
Fraunhofer IKTS ()
lithium-ion battery; process monitoring; acoustic measurement; lamination; machine learning; Artificial Neural Networks; Convolutional Neural Networks; lamination technology

Due to the energy transition and the growth of electromobility, the demand for lithium-ion batteries has increased in recent years. Great demands are being placed on the quality of battery cells and their electrochemical properties. Therefore, the understanding of interactions between products and processes and the implementation of quality management measures are essential factors that requires inline capable process monitoring. In battery cell lamination processes, a typical problem source of quality issues can be seen in missing or misaligned components (anodes, cathodes and separators). An automatic detection of missing or misaligned components, however, has not been established thus far. In this study, acoustic measurements to detect components in battery cell lamination were applied. Although the use of acoustic measurement methods for process monitoring has already proven its usefulness in various fields of application, it has not yet been applied to battery cell production. While laminating battery electrodes and separators, acoustic emissions were recorded. Signal analysis and machine learning techniques were used to acoustically distinguish the individual components that have been processed. This way, the detection of components with a balanced accuracy of up to 83% was possible, proving the feasibility of the concept as an inline capable monitoring system.