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.

With increasing electrification in the automotive field, lithium‐ion batteries are rapidly becoming an inseparable part of everyday life. To meet the various governmental goals regarding CO2 emissions, it has become imperative to rapidly optimize the manufacturing process to produce high‐quality batteries at the least possible emissions and cost. Model‐based methods provide a simple and efficient view on complex processes and on identifying best‐case scenarios for production, since they require minimal material and time expenditure. In the authors’ recently published work, by Thomitzek et al., a digital modeling framework is initially described. It uniquely combines process chain and battery cell models. Herein, this digital framework is utilized to set up a numerical optimization routine. The routine helps to identify the best possible microstructure parameters in an NMC 622 cathode to maximize the resulting discharge volumetric energy density. Furthermore, the minimal energy expenditure for processing is determined. With the findings herein, an inexpensive method for identifying optimal battery manufacturing scenarios is presented, with the goal of producing high‐quality battery cells at the lowest cost. The provided model framework and optimization routine is easily adaptable for other battery types and manufacturing lines.

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.