Understanding Electrolyte Filling to Improve the Performance of Lithium-Ion Batteries: A Pore-Scale Study

Abstract

The cell production of lithium-ion batteries is predicted to increase exponentially in the upcoming years. Therefore, the optimization of the cell manufacturing is key to reduce costs and also necessary to improve the battery performance. In this context, the filling of cells with liquid electrolyte has gained attention mainly from experimentalists. However, most of the experimental methods applied are complex and time-consuming, suffer from low spatial or temporal resolution, and can hardly resolve interdependencies of influencing factors. Thus, a comprehensive understanding especially of pore-scale phenomena during the filling process is still missing. In this study, mesoscopic computational approaches are used to investigate electrolyte filling and its effects on battery performance on the pore scale. Those are the lattice Boltzmann method (LBM) and pore network models (PNM). A common LBM approach is applied and combined with a recently developed homogenization approach to study flow in pores of different length scales simultaneously. Detailed simulations in realistic 3D reconstructions of lithium-ion battery cell components, such as different electrodes and separators, are conducted. 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. In this respect pressure-saturation curves are a characteristic property of porous media and relate the pressure difference needed for invasion to the amount of electrolyte in the pore space. Our intrusion simulations indicate a significant gas entrapment at the end of the filling process. Moreover, due the high structural resolution of LBM, detailed information about the spatial distribution of the gas in the pore space can be provided. This is analyzed in detail to show how the gas adversely affects the battery performance by reducing effective transport properties and electrochemically active surfaces. In addition, the pressure-saturation results are used to develop a new and efficient PNM approach which works on a strongly simplified basis regarding the pore geometry and the physics that are solved. It is based on a physically motivated geometrical shape correction and is shown to reproduce the LBM results quite well. The characteristic pressure levels are predicted correctly, while compared to LBM, the computational time needed is reduced from days to minutes. This study shows that both LBM and PNM are useful to understand the electrolyte filling process. Using the computationally demanding but very detailed LBM, results indicate how the filling process, the final degree of electrolyte saturation, and potentially also the battery performance can be optimized. Being only interested in the pressure-saturation behavior, the new PNM can give sufficient insight. It can also be used to efficiently scan parameter spaces to give first indications based on which more detailed simulations, such as LBM, can be conducted. Thus, each method has its specific strengths and their full potential can be achieved when being applied complementary. All in all, both methods are powerful tools in supporting 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. The simulations were carried out on the Hawk at the High Performance Computing Center Stuttgart (HLRS) under the grant LaBoRESys, and on JUSTUS 2 at the University Ulm under the grant INST 40/467-1 FUGG.