The growing demand for lithium‐ion batteries in electric vehicles pose challenges, particularly for the use of critical materials such as cobalt in electrode slurry mixing. Due to regulatory requirements such as the battery passport, an accurate traceability system is mandatory. However, there is no solution for the continuous mixing process, which is a major challenge in electrode production. This article describes an approach to solve this problem. Based on the results of a literature review, a design structure matrix is constructed to analyze parameter influences. The overall result is a list of relevant parameters that should be recorded in the continuous mixing process in order to characterize the mixing process.

Due to high scrap rates and manufacturing costs, battery cell production requires continuous process optimization. The potential for material efficiency is particularly high in electrode production, specifically in the mixing process. Challenges in the continuous mixing process are related to automation and traceability of material. As one of the most relevant parameters, the residence time of particles must be known, otherwise it is not possible to make a statement about the traceability of the slurry ingredients. Without knowledge of the residence time distribution (RTD), autonomous process control or traceability of battery cells and their components is not possible. The influence of process and material parameters on the RTD of the continuous mixing process in battery cell production is being systematically investigated. Based on a design of experiment, the mean residence time and the RTD are determined for a graphite‐based anode slurry by manipulating the conductivity by adding a tracer. Special attention is given to the properties of the tracer as well as the tracer behavior within the mixing process. The influence of different parameters is analyzed based on the conductivity changes. It is shown that the parameters mass flow and solid content have the greatest influence on the RTD.

The development of interoperable and reusable information models is a key challenge for digitalization in manufacturing domains with heterogeneous and complex process chains. Ensuring seamless data exchange requires the standardization of both data syntax and semantics, while maintaining compatibility with existing industry standards. This paper presents a methodology for deriving standardizable and generalizable OPC UA information models tailored to domains with high process variability and interdisciplinary requirements. The methodology integrates system analysis, parameter mapping, and the development of modular submodels, supported by expert input and validation. It emphasizes the reuse and extension of existing OPC UA Companion Specifications to reduce complexity, avoid redundancy, and enable long-term standardization. The approach is exemplified by its application to battery cell production, an emerging manufacturing domain combining process and mechanical engineering with continuous and discrete processes. Its high degree of heterogeneity and lack of domain-specific standards pose significant challenges for model development. Through iterative expert workshops and structured model validation, a dedicated and transferable OPC UA framework is created. The resulting layered model structure combines a cross-industry standard with newly developed, process-aware model elements. This enables both broad applicability and the depth required for complex production environments, while supporting use cases such as traceability, regulatory reporting (e.g., EU Battery Passport), and process optimization. The resulting model improves interoperability, transparency, and data integration, offering a scalable blueprint for other complex manufacturing sectors.

Battery cell production is costly, especially due to expensive raw materials and high scrap rates, making continuous process optimization essential. Electrode manufacturing, particularly the mixing process that defines the fundamental electrochemical properties of a battery, offers great potential for increasing material efficiency. Understanding the residence time and residence time distribution (RTD) of particles within the continuous mixing process is crucial for optimization. Autonomous process control requires accurate prediction of the RTD to enable precise and resource‐efficient rejection of nonconforming material. Similarly, knowledge of the RTD is required to ensure the traceability of battery cell materials within the mixing process. A model developed from experimental data is presented that simulates the RTD of a twin‐screw extruder based on the input process parameters screw speed, mass flow, and solid content. This is possible for the entire system and the subsystems process section, solvent dosing unit, binder dosing unit, and powder dosing unit. As a result, the characteristic times can be predicted with a very high degree of accuracy from when the raw materials are added to the final slurry at the end of the continuous mixing process. This enables resource‐efficient production and provides detailed information for traceability in continuous mixing processes.

In der Batterieproduktion werden zukünftig Extruder zum Slurry-Mischen eingesetzt. Die fehlenden Erfahrungen hierbei sind bei der Prozesssteuerung und -optimierung eine Herausforderung. Daher wurde ein digitaler Zwilling (DT) eines Extruders samt funktionalen Restriktionen mit der Software Siemens NX und Siemens MCD aufgebaut. Weiter wurde ein Tool zur digitalen Validierung des Steuerungscodes im DT hinsichtlich Funktionssicherheit entwickelt. Dies ermöglicht eine Validierung und Optimierung des Codes bereits in den frühen Phasen der Steuerungsentwicklung. In the future, extruders will be used for slurry mixing in battery production. The lack of experience is a challenge when it comes to process control and optimization. Therefore, a digital twin (DT) of the extruder including functional restrictions was developed with Siemens NX and Siemens MCD. Also, a tool for the digital validation of the control code in the DT with regard to functional reliability was developed. This allows for validating and optimizing the code in the early stages of control development.