Lithium-ion batteries are a key technology for electromobility; thus, quality control in cell production is a central aspect for the success of electric vehicles. The detection of defects and poor insulation behavior of the separator is essential for high-quality batteries. Optical quality control methods in cell production are unable to detect small but still relevant defects in the separator layer, e.g., pinholes or particle contaminations. This gap can be closed by executing high-potential testing to analyze the insulation performance of the electrically insulating separator layer in a pouch cell. Here, we present an experimental study to identify different separator defects on dry cell stacks on the basis of electric voltage stress and mechanical pressure. In addition, finite element modeling (FEM) is used to generate physical insights into the partial discharge by examining the defect structures and the corresponding electric fields, including topographical electrode roughness, impurity particles, and voids in the separator. The test results show that hard discharges are associated with significant separator defects. Based on the study, a voltage of 350 to 450 V and a pressure of 0.3 to 0.6 N/mm² are identified as optimum ranges for the test methodology, resulting in failure detection rates of up to 85%.

In today’s cell production, the focus lies on maximizing productivity while maintaining product quality. To achieve this, the lamination of electrode and separator is one key process technology, as it bonds the electrode and separator to form mechanically resilient intermediate products. These mechanically resilient intermediates are necessary to enable high throughput processes. Although the lamination process has significant effects on the electrochemical performance of battery cells, it has not been sufficiently researched with regard to its process-product interdependencies. Therefore, this paper addresses the investigation of these interdependencies and proposes three characterization methods (grey scale analysis, high potential tests, electrochemical cycling and C-rate tests). The results of the three methods show that the lamination process with its process parameters (lamination temperature, lamination pressure and material feed rate) has an influence on both the properties of the intermediate product and the cell properties. In conclusion, the knowledge of the process-product interdependencies is essential in order to utilize the advantages of lamination integrated into the process chain and consequently achieve quality-assured cell production.

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.

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.

Battery cell production is characterized by energy‐intensive processes that contribute significantly to the overall environmental impact of the battery life cycle. Energy demand values reported in the literature vary widely, mainly due to different assumptions and scales considered. In addition, detailed process and material information is often missing. This lack of transparency hinders the comparison of different production strategies (e.g., scales, technologies) and product characteristics (e.g., battery types and sizes, materials). To provide a common basis for calculating the energy demand in battery cell production, this work presents process‐specific energy models for electrode production, cell assembly, conditioning, and dry room. Factors related to material flow, production scale, and electrode structure are also considered in the modeling approach. The most relevant models are validated with data from literature and experiments on a pilot‐line scale. Further results include a sensitivity analysis to investigate the effects of process parameters on process‐specific and total energy demand. Moreover, the effects of increasing equipment capacity and production scale on energy demand are investigated. The results agree with ranges reported in the literature and demonstrate the methodology's capability to calculate the energy demand of different processes, considering real machine specifications as well as production and product characteristics.