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Energy Technology
Article . 2022 . Peer-reviewed
License: CC BY NC ND
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Model‐Based Design and Experimental Evaluation of a High‐Throughput Electrode Feeding and Stacking Process

Authors: Christina von Boeselager; Alexander Müller; Leif Tönjes; Xuenan Shi; Dominik Wassenberg; Daniel Evans; Arne Glodde; +2 Authors

Model‐Based Design and Experimental Evaluation of a High‐Throughput Electrode Feeding and Stacking Process

Abstract

To provide storage capacities for the emerging markets of electromobility and stationary applications, the increase of productivity within the production of lithium‐ion batteries is crucial. A special focus lies on the assembly of the electrode–separator–compound as a bottleneck process within battery cell production. Consequently, novel process technologies arise for high‐throughput assembly technologies. However, complex process–product interactions drive complexity within the design of new processes. Process models, experiments, and simulations support the process design but must depict nonlinear material behavior. This increases the expanse in time and resources for the process design. Herein, an approach to reduce the effort of process design using the example of the assembly of the electrode–separator–compound is shown. The approach aims to identify and select solutions within the process design. Analytical, simulative, and experimental methods and tools are applied within the approach to investigate the design solutions at different levels of detail. The practical application of the approach is demonstrated in two case studies within the assembly of the electrode–separator–compound. The results of the case studies show a profound choice of the process design and a gain in knowledge on process–product–interactions of the novel processes.

Country
Germany
  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    3
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
hybrid