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Herein, a discrete element method (DEM) approach is proposed to investigate the impact of the calendering process on the electrical and ionic conductivities and on the adhesion strength of Li[Ni1/3 Mn1/3 Co1/3]O2 (NMC)‐based electrodes. For this purpose, key correlations between the microstructure and these electrode‐scale properties are established using the outcomes of the simulations and real experiments. In addition, the evolution of the structure and the development of mechanical stress are also studied numerically during electrochemical cycling, offering a closer insight into the intercalation mechanism. Finally, the impact of the initial noncalendered porosity on the electrode mechanical response is examined, showing that higher initial porosities lead to lower final porosities under same calendering loads. Overall, this work demonstrates the potential of DEM simulations in improving the understanding of the microstructure and mechanics of lithium‐ion electrodes.

All-solid-state batteries constitute a very promising energy storage device. Two very important properties of these battery cells are the ionic and the electrical conductivity, which describe the ion and the electron transport through the electrodes, respectively. In this work, a numerical method is presented to model the electrical conductivity, considering the outcome of discrete-element method simulations and the intrinsic conductivities of both the active material particles and the conductive additive particles. The results are calibrated and validated with the help of experimental data of real manufactured electrodes. The tortuosity, which strongly influences the ionic conductivity, is also presented for the analyzed electrodes, taking their microstructure into account.

In this paper, a widely mechanistic model was developed to depict the rheological behaviour of nanoparticulate suspensions with solids contents up to 20 wt.%, based on the increase in shear stress caused by surface interaction forces among particles. The rheological behaviour is connected to drag forces arising from an altered particle movement with respect to the surrounding fluid. In order to represent this relationship and to model the viscosity, a hybrid modelling approach was followed, in which mechanistic relationships were paired with heuristic expressions. A genetic algorithm was utilized during model development, by enabling the algorithm to choose among several hard-to-assess model options. By the combination of the newly developed model with existing models for the various physical phenomena affecting viscosity, it can be applied to model the viscosity over a broad range of solids contents, shear rates, temperatures and particle sizes. Due to its mechanistic nature, the model even allows an extrapolation beyond the limits of the data points used for calibration, allowing a prediction of the viscosity in this area. Only two parameters are required for this purpose. Experimental data of an epoxy resin filled with boehmite nanoparticles were used for calibration and comparison with modelled values.