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Numerical Models of the Electrolyte Filling Process of Lithium-Ion Batteries to Accelerate and Improve the Process and Cell Design

Authors: Jan Hagemeister; Florian J. Günter; Thomas Rinner; Franziska Zhu; Alexander Papst; Rüdiger Daub;

Numerical Models of the Electrolyte Filling Process of Lithium-Ion Batteries to Accelerate and Improve the Process and Cell Design

Abstract

In order to meet consumer demands for electric transportation, the energy density of lithium-ion batteries (LIB) must be improved. Therefore, a trend to increase the overall size of the individual cell and to decrease the share of inactive materials is needed. The process of electrolyte filling involves the injection of electrolyte liquid into the cell, as well as the absorption of the electrolyte into the pores of the electrodes and the separator, which is known as wetting. The trend towards larger-format LIB challenges the electrolyte filling due to an increase in wetting distance for the electrolyte as well as a decrease in the void volume of the cell. The optimization of the process via numerical simulation promises to reduce costs and ensure quality during battery production. The two models developed in this study are based on a commercial computational fluid dynamics (CFD) program to study the effect of process parameters, such as pressure and temperature, on the filling process. The results were verified with neutron radiography images of the dosing process and a feasibility study for a wetting simulation is shown. For all simulations, specific recommendations are provided to set up the electrolyte filling process, based on which factors generate the greatest improvement.

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Keywords

TK1001-1841, computational fluid dynamics, battery production, simulation, TP250-261, Production of electric energy or power. Powerplants. Central stations, Industrial electrochemistry, electrolyte filling, lithium-ion cells; battery production; electrolyte filling; computational fluid dynamics; simulation, lithium-ion cells, ddc: ddc:

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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!
19
Top 10%
Average
Top 10%
Green
gold