Ensuring maximum quality at minimum cost - this is what scientists want to achieve in a new research platform that has been launched at KIT as part of the battery competence cluster AQua (which stands for: Analytics/Quality Assurance). To this end, they are first looking at every production step, from the starting materials to the finished cell, in order to identify possible sources of error. The aim is then to optimize and automate the handling of errors during ongoing production so that consistently high quality can be guaranteed in the end. "Every step in production has to be right. Everything is coordinated and every error can have an impact on the subsequent performance of the cells," says Professor Helmut Ehrenberg from the Institute for Applied Materials (IAM-ESS) at KIT, who is coordinating the research work. "The performance of analytics and quality assurance therefore has a significant influence on the quality, safety and costs of a cell." The researchers are developing their solutions using integrated production control methods and implementing them into the entire process chain.

Research into automatic fault detection

Production of an electrode stack in the laboratory. Semi-automated production systems ensure that production remains flexible. © Markus Breig, KIT

In order to identify critical errors in the production process as early as possible and to interpret them correctly, the scientists in the AQua projects work according to the principle of Failure Mode and Effects Analysis (FMEA), among other things. "We introduce errors in a targeted and controlled manner in order to precisely quantify the relationship between a malfunction and the effects on the cells," explains Dr. Lea de Biasi, one of the researchers in the project. "If we now define specific performance criteria as quality targets, we can set tolerance limits for all relevant process steps." These are then used directly in the production process. To this end, AQua is also developing methods with which critical influencing variables - such as the homogeneity of the electrode coating or the residual moisture of the components at the start of cell construction - can be recorded in real time. During automatic defect detection, intermediate products are automatically inspected immediately after the respective process step and defective pieces are sorted out. "It is also possible to draw conclusions about the causes of defects," says de Biasi. "This allows us to eliminate process faults at an early stage and avoid further costs due to rejects."

Data infrastructure for rapid research transfer

The new research platform is complemented by an accompanying project coordinated at KIT by Dr. Michael Selzer from the Institute for Applied Materials - Computational Materials Science (IAM-CMS). Among other things, this involves the exchange of the platform with the other battery competence clusters of the "Battery Research Factory" and cooperation with industry. However, the focus is on setting up a data infrastructure: "The experiments and large-scale simulations in the AQua project generate large amounts of data that need to be evaluated using specific data analysis methods via standardized workflows," says Selzer. "With the data infrastructure, we are creating sustainable access to this research data and analysis tools." Professor Britta Nestler from the IAM-CMS, who is a key supporter of the project as an expert in microstructure simulation, emphasizes that this is a decisive contribution to quality assurance and also to the transfer of research in battery production. "In AQua, we want to develop a comprehensive and cross-process understanding of how the interaction of materials, production steps and electrochemical characteristics affects the structures and properties of the battery."