Lithium-ion batteries are the main technology for storing electrical energy with applications ranging from electric vehicles to portable devices. However, energy density as well as power density have to be further improved due to the steadily growing demand for high-performance cells. One possible approach to tackle this challenge are structured electrodes, where differently sized active particles are either mixed within a single-layer electrode or used in a two-layer setting. In this contribution, we present a parametric stochastic 3D microstructure model that is calibrated to tomographic image data of experimentally manufactured cathodes. For this purpose, the modeling framework presented in [1] is adapted. On the one hand, this allows us to generate virtual, but realistic electrodes, which are statistically equivalent to the manufactured ones. On the other hand, the systematic variation of the model parameters enables the simulation of a broad range of structured single-layer as well as two-layer electrodes, which have not yet been manufactured. In combination with spatially resolved numerical simulations of electrochemical properties, as for example carried out in [2], this data-driven modeling approach can be used to provide recommendations for an optimal design of structured electrodes regarding their electrochemical properties.

[1] Daniel Westhoff, Ingo Manke, and Volker Schmidt. Generation of virtual lithium-ion battery electrode microstructures based on spatial stochastic modeling. Computational Materials Science 151, 53-64, 2018.

[2] Vittorio De Lauri, Lukas Krumbein, Simon Hein, Benedikt Priing, Volker Schmidt, Timo Danner, and Arnulf Latz. Beneficial effects of three-dimensional structured electrodes for the fast charging of lithium-ion batteries. ACS Applied Energy Materials 4 (12), 13847-13859, 2021.