Filling electrodes with electrolyte is a time-critical battery manufacturing step that also affects the battery performance. Most of the physical phenomena during the filling occur on the pore scale and are hard to study experimentally. Therefore, in this work, computational approaches are used to study filling processes and the corresponding pore-scale phenomena. Using the lattice Boltzmann method (LBM), electrolyte flow in 3D lithium-ion battery cathodes with and without binder is simulated with high spatial resolution. The results are used to adjust and validate pore network models (PNM). These work on a strongly simplified basis regarding the pore geometries and the physics that are solved, but are computationally extremely efficient. Therefore, an interconnection and complementarity of both aforementioned methods is a desirable goal. The approach proposed here is universal and can be generally applied to investigate filling of other porous media, such as battery components or energy storage devices.
The influence of a broad variety of structural and physico-chemical properties of the active material and binder as well as process parameters is studied using LBM. Pressure-saturation curves are determined. They are used to develop a new PNM that reproduces these results using a physically motivated geometrical shape correction. The results of both approaches are in good accordance. The characteristic pressure levels are well predicted by the new PNM, which is especially remarkable as the computational time was coincidently reduced from days to minutes.
However, due to its sound physical basis, LBM has also some unique features which cannot be reproduced using PNM. For example, the LBM simulations suggest a systematic entrapment of residual gas in the pores. Therefore, detailed analyses are conducted which yield a strong interdependency of the amount, spatial distribution, and size distributions of the gas agglomerates. Moreover, it is shown how the residual gas can adversely affect the battery performance by reducing effective transport properties and electrochemically active surfaces.
This study shows that both LBM and PNM are useful to understand flow in porous media. For a detailed insight into more complex physical phenomena at the pore scale, LBM should be used. Nevertheless, each method has its specific strengths and their full potential can be achieved when being applied complementary. The results indicate how the filling process, the final degree of electrolyte saturation, and potentially also the battery performance can be optimized. Thus, both methods can be powerful tools in supporting, e.g, electrode, electrolyte, and process design in the context of battery development.