Simulation-Based and Data-Driven Techniques for Quantifying the Influence of the Carbon Binder Domain on Electrochemical Properties of Li-Ion Batteries

Tobias Knorr, Simon Hein, Benedikt Prifling, Matthias Neumann, Timo Danner, Volker Schmidt and Arnulf Latz
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Tobias Knorr: German Aerospace Center (DLR), Institute of Engineering Thermodynamics, 70569 Stuttgart, Germany
Simon Hein: German Aerospace Center (DLR), Institute of Engineering Thermodynamics, 70569 Stuttgart, Germany
Benedikt Prifling: Institute of Stochastics, Ulm University (UUlm), 89081 Ulm, Germany
Matthias Neumann: Institute of Stochastics, Ulm University (UUlm), 89081 Ulm, Germany
Timo Danner: German Aerospace Center (DLR), Institute of Engineering Thermodynamics, 70569 Stuttgart, Germany
Volker Schmidt: Institute of Stochastics, Ulm University (UUlm), 89081 Ulm, Germany
Arnulf Latz: German Aerospace Center (DLR), Institute of Engineering Thermodynamics, 70569 Stuttgart, Germany

Energies, 2022, vol. 15, issue 21, 1-19

Abstract: Most cathode materials for Li-ion batteries exhibit a low electronic conductivity. Therefore, a considerable amount of conductive additives is added during electrode production. A mixed phase of carbon and binder provides a 3D network for electron transport and at the same time improves the mechanical stability of the electrodes. However, this so-called carbon binder domain (CBD) hinders the transport of lithium ions through the electrolyte and reduces the specific energy of the cells. Therefore, the CBD content is an important design parameter for optimal battery performance. In the present study, stochastic 3D microstructure modeling, microstructure characterization, conductivity simulations as well as microstructure-resolved electrochemical simulations are performed to identify the influence of the CBD content and its spatial distribution on electrode performance. The electrochemical simulations on virtual, but realistic, electrode microstructures with different active material content and particle size distributions provide insights to limiting transport mechanisms and optimal electrode configurations. Furthermore, we use the results of both the microstructure characterization and electrochemical simulations to deduce extensions of homogenized cell models providing improved predictions of cell performance at low CBD contents relevant for high energy density batteries.

Keywords: Li-ion battery; microstructure-resolved simulation; carbon binder domain; thick electrode; stochastic 3D microstructure modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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