Targeted Data Generation in the Continuous Production of Anode Slurries for Lithium-Ion Battery Cells

This research paper explores the transition from batch mixing to continuous mixing processes for the production of slurries used in lithium-ion battery cells. The conventional batch mixing methods, prevalent in European industry, suffer from time-consuming cleaning processes, lengthy mixing times, and the inability to monitor paste conditions in real-time. The study proposes a solution using continuous mixing with a twin-screw extruder, highlighting advantages such as reduced cleaning time, immediate sample characterization, and minimized production risks. The research aims to develop a test setup and procedure to accelerate the understanding of the continuous mixing process, leveraging minimal resources and time, and facilitating the training of artificial intelligence algorithms. The ultimate goal is to create a digital twin encompassing all influencing factors for predictive and prescriptive analytics. The methodology involves literature reviews, expert surveys, and experimental setups, with an emphasis on inline measuring devices. The paper presents results related to relevant product characteristics, influencing factors, and the selection of suitable measuring equipment. The experimental setup includes a database structure, human-machine interface, and visualization of measurement data. The study concludes with insights into the correlation analysis of influencing factors and product characteristics, emphasizing the need for further experiments and the development of algorithms for predictive quality assessments in continuous mixing processes.