Enhanced Processing and Testing Concepts for New Active Materials for Lithium‐Ion Batteries
Electrode manufacturing requires multiple process steps, e.g., dispersing and coating. In‐between these steps, intermediate products have to be transferred, stored, and handled. Especially for the development of new active materials or electrode formulations, the variety of parameters that need to b...
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Veröffentlicht in: | Energy technology (Weinheim, Germany) Germany), 2020-02, Vol.8 (2), p.n/a |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Electrode manufacturing requires multiple process steps, e.g., dispersing and coating. In‐between these steps, intermediate products have to be transferred, stored, and handled. Especially for the development of new active materials or electrode formulations, the variety of parameters that need to be screened is enormous. In addition, these materials are initially tested in small batches, and it is not always possible to upscale the used processes. To evaluate the performance of different materials or differently processed materials, test cells are assembled. This usually requires manual work procedures, which are inherently sensitive to variations and untraceable errors. If the stochastic flaws are large enough, the effects of process variations are covered by these. It is therefore important to increase reproducibility in all process steps. Herein, new concepts for electrode production and automated sample preparation for highly reproducible production and more effective electrode development and screening of parameters are presented. A combined grinding and dispersion process for the production of silicon‐based anodes and an automated assembly system for efficient testing is presented. The processes are supported by methods of data mining to collect process data, ensure high reproducibility, and support research on new active materials.
To reveal the effects of parameter variations in electrode production for lithium‐ion batteries, the prevention of undefined errors in the process chain is crucial. For targeted development, new concepts for suspension production and reproducible sample preparation, supported by methods of data mining, are proposed. |
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ISSN: | 2194-4288 2194-4296 |
DOI: | 10.1002/ente.201900133 |