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 the filling process 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) which in comparison to LBM are computationally very efficient. The methodology proposed here is universal and can be generally applied to filling of other 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. Pressure-saturation curves are determined and suggest a systematic entrapment of residual gas in the pores. A detailed analysis yields 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.
The results indicate how the filling process, the final degree of electrolyte saturation, and potentially also the battery performance can be optimized. The most favorable results are observed for electrodes with large pores and a good connectivity of the pore space as well as a strong wettability of the solid electrode components.
Altogether, it is shown that both computational methods, i.e. LBM and PNM, yield a detailed insight into the influencing factors of filling processes on the pore scale and can be used to support the electrode, electrolyte, and process design.
This work has been funded by European Union’s Horizon 2020 research and innovation programme within the research project DEFACTO under grant agreement Nº875247.