Analysis of Quality‐Relevant Process–Structure Relationships through Tracking and Tracing: A Comprehensive Study on the Calendering Process in Lithium‐Ion Battery Production

The calendering process represents the final step in the electrode manufacturing for lithium‐ion batteries. This process significantly influences and adjusts electrochemical and mechanical parameters. The major challenges are the unknown cause‐effect relationships as well as the high setup effort to reach desired electrode parameters. This article presents an innovative approach using an integrated marker‐based traceability system to link machine and sensor data to key inline production parameters during electrode calendering. Offline measurements are generated after calendering, which are assigned to individual electrode sections via the applied markings. This allowed correlations to be derived between the set machine parameters, the measured inline data, and subsequently the measured offline data. Using a stepwise experimental plan, an electrode is calendered, and its compaction behavior is extensively characterized. From the determined relationships, the machine parameters to be adjusted for targeted electrode properties can be efficiently derived. The types of defects that occur with increasing calendering degree are quantified using the appropriate measurement technology and traced back to the set machine parameters. The implementation of this approach enables a substantial reduction in the setup effort required for the calendering process, necessitating a limited degree of experimental effort.

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Publication Details

Journal
Batteries & Supercaps
Published
2025-03-17
DOI
https://doi.org/10.1002/batt.202500133
Citations
3
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
3.64
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article

Analysis of Quality‐Relevant Process–Structure Relationships through Tracking and Tracing: A Comprehensive Study on the Calendering Process in Lithium‐Ion Battery Production

Alessandro Sommer, Andreas Mayr, J. Schächtl, Rüdiger Daub et al.
3 citations
Batteries & Supercaps
Fault Detection and Control Systems
3.64
article

Analysis of Quality‐Relevant Process–Structure Relationships through Tracking and Tracing: A Comprehensive Study on the Calendering Process in Lithium‐Ion Battery Production

Alessandro Sommer, Andreas Mayr, J. Schächtl, Rüdiger Daub, Julian Link
article en
3 citations

Abstract

The calendering process represents the final step in the electrode manufacturing for lithium‐ion batteries. This process significantly influences and adjusts electrochemical and mechanical parameters. The major challenges are the unknown cause‐effect relationships as well as the high setup effort to reach desired electrode parameters. This article presents an innovative approach using an integrated marker‐based traceability system to link machine and sensor data to key inline production parameters during electrode calendering. Offline measurements are generated after calendering, which are assigned to individual electrode sections via the applied markings. This allowed correlations to be derived between the set machine parameters, the measured inline data, and subsequently the measured offline data. Using a stepwise experimental plan, an electrode is calendered, and its compaction behavior is extensively characterized. From the determined relationships, the machine parameters to be adjusted for targeted electrode properties can be efficiently derived. The types of defects that occur with increasing calendering degree are quantified using the appropriate measurement technology and traced back to the set machine parameters. The implementation of this approach enables a substantial reduction in the setup effort required for the calendering process, necessitating a limited degree of experimental effort.

Batteries & SupercapsVol. 8(10)
Fraunhofer Institute for Machine Tools and Forming Technology (DE), Fraunhofer Institute for Casting, Composite and Processing Technology IGCV (DE), Technical University of Munich (DE)
Openalex Percentile: Top 8%
Fault Detection and Control Systems
3.64
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