Although, lithium-ion batteries (LIB) are a mature and promising technology for the application in electro-mobility and large-scale energy storage, there are still number of technological challenges to be addressed. One important aspect is the manufacturing process that is still not fully optimized. An example is the electrolyte filling process, which can have a remarkable impact on power and performance.

Most physical phenomena that influence the filling process occur at the pore scale and are therefore hard to study experimentally. Modelling and simulations can overcome these limitations and provide valuable insight. A further advantage is that large parameter spaces are relatively easy to study compared to experiments.

In this work, we present a workflow which is especially favorable for such studies. It couples a multi-component fluid flow lattice Boltzmann method (LBM) to a pore network model (PNM) to get a detailed insight into the relevant parameter combinations for the electrolyte filling process. While LBM simulations solve complex transport equations with high structural resolution of the porous medium, the PNMs approximate the real structure as a network of pores that has the same topology and comparable geometrical properties. The resulting simulations are computationally very efficient. However, the reduction in complexity comes at the cost of accuracy and temporal resolution.

Therefore, PNMs cannot replace LBM simulations entirely. One needs a small set of LBM results to calibrate the PNM via geometrical corrections. With this correction, the calibrated PNM very accurately predicts important material characteristics, such as pressure-saturation behavior, when compared to the full-scale LBM simulations.

The LBM-coupled PNM allows for comprehensive insight into the complex dynamics of electrolyte filling at relatively low computational cost. The data can be used to derive important properties of materials or identify new promising material combinations. When probing large parameter spaces, simulations are much faster and cheaper than experiments while yielding a high level of detail at same time.