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.

As the demand for lithium‐ion batteries (LIBs) continuously grows, the necessity to improve their efficiency/performance also grows. For this reason, optimization of the individual production steps is critical. Calendering is a crucial production step whereby electrode coatings are compacted to targeted densities. This process affects the porosity, adhesion, thickness, wettability, and charge transport properties of the electrodes, as well as the homogeneity of the coatings. Optimal calendered electrodes improve volumetric energy density, cyclic stability, and rate capability of the cells and also enhance the structural stability of the active material, which affects electrode safety and polarization. This article outlines the fundamental processes and mechanisms, as well as how modeling, simulation, and tomography can be used to optimize these processes. Additionally, the influence of calendering on a wide range of anode and cathode active materials is discussed. This review serves to give a deeper understanding into the calendering process‐structure‐performance relationships, and how they can be optimized to improve the performance of LIBs.

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.

Lithium‐ion batteries are used in a wide range of applications, with the electromobility sector being the main contributor to the increasing demand predicted for the next decade. Although batteries play an important role in decarbonizing the transportation sector, their production includes energy‐intensive processes that hinder a more sustainable production. Moreover, the production processes are characterized by a manifold of parameters leading to complex cause–effect relations along the process chain which influences the battery cell quality. Therefore, a sustainable future for battery production and the electromobility sector depends on the environmentally and economically efficient production of high‐performance batteries. Against this background, this work presents a digitalization platform based on the coupling of mechanistic models to digitally reproduce the battery cell production and provide a deeper understanding of the interdependencies on the process, production, and product levels. In addition to a description of the individual models contained in the platform, this work demonstrates their coupling on a use case to study the effects of different solids contents of the coating suspension. Besides providing a multilevel assessment of the parameter interdependencies, considering quality, environmental and economic aspects, the presented framework contributes to knowledge‐based decision support and improvement of production and battery cell performance.