In the drying process of electrodes for lithium-ion batteries, the layer structure is defined and can only be influenced slightly in the subsequent process steps. An essential point in the drying process is the fixation of the binder, ensuring both the adhesive and cohesive strength of the electrode. It is known that high drying rates lead to the segregation of the binder in the direction of the coating surface, which results in reduced mechanical stability of the electrode. In a previous publication, an experimental approach was used to investigate the underlying processes that influence binder migration. These results are now used in a model-based approach to describe the binder migration using the convection–diffusion equation. The convective term originates from the shrinkage behavior of the layer during drying due to the relative movement between the active material particles and the solvent in which the binder is dissolved or dispersed; it is expected to be the cause of the binder migration. The diffusive term, representing the binder movement in the solvent, counteracts segregation. The interaction of these forces is simulated at different drying temperatures and the associated drying rates.

Lithium‐ion batteries are state‐of‐the‐art and still their performance is subject to constant improvement. These enhancements are based, among other things, on optimization in the electrode production process chain. High optimization potential exists for the drying process of electrodes, as aiming for high drying speeds can greatly reduce both, investment costs and operating costs of the drying. However, high drying rates without appropriate precautions go hand in hand with poorer cell performance and adhesive strength, leading to a conflict between the required performance and production costs of the electrodes. Herein, a numerical approach based on the discrete element method to describe the formation of the electrode structure during drying is presented. The focus is placed on the active material structure and the effects due to particle interactions. Herein, a direct numerical description of the fluid phase is avoided by using various fluid substitute models, so that the simulation time and the computational costs can be greatly reduced. The model is validated by simulating different electrode areal loadings and comparing the achieved layer thicknesses to experimental results of the electrode drying process. A high agreement between experiment and simulation regarding density is obtained for different areal loadings.

Drying is a critical process step during battery electrode production due to its microstructure defining nature. Distribution and interconnectivity of active material particles, pores, and the overall porosity significantly influence the later cell performance. Knowledge about structure formation as well as electrode property prediction are crucial for optimization and targeted electrode design. However, the exact microprocesses during electrode drying are not yet understood well and very difficult to access experimentally. Therefore, in this study, a combination of computational fluid dynamics (CFD) and discrete element method (DEM) simulation models for investigating structure formation considering particle–particle as well as fluid‐particle interactions and vice versa is presented. , The volume of fluid method is used for taking into account the fluid‐fluid interface, evaporation and capillary interactions. The simulations reveal the formation of a top–down consolidation front which interacts with the fluid leading to a backflow of liquid. The results show good agreement with experimental measurements for NMC622 cathodes. Furthermore, the influence of production parameters such as mass loading and solids content is examined. The findings demonstrate the modeling tool's suitability for process engineers to anticipate and enhance electrode characteristics and facilitate scientists to understand complex structure formation relationships.

The application of batteries in electric vehicles and stationary energy-storage systems is widely seen as a promising enabler for a sustainable mobility and for the energy sector. Although significant improvements have been achieved in the last decade in terms of higher battery performance and lower production costs, there remains high potential to be tapped, especially along the battery production chain. However, the battery production process is highly complex due to numerous process–structure and structure–performance relationships along the process chain, many of which are not yet fully understood. In order to move away from expensive trial-and-error operations of production lines, a methodology is needed to provide knowledge-based decision support to improve the quality and throughput of battery production. In the present work, a framework is presented that combines a process chain model and a battery cell model to quantitatively predict the impact of processes on the final battery cell performance. The framework enables coupling of diverse mechanistic models for the individual processes and the battery cell in a generic container platform, ultimately providing a digital representation of a battery electrode and cell production line that allows optimal production settings to be identified in silico. The framework can be implemented as part of a cyber-physical production system to provide decision support and ultimately control of the production line, thus increasing the efficiency of the entire battery cell production process.