KIProBatt: Intelligent battery cell production with AI-supported process monitoring

For process monitoring in a battery cell production plant, a hybrid simulation is being created that combines elements of a multi-scale simulation with data analytics methods (HYPASIM subproject). The hybrid model will be integrated into the generic system architecture of the overall KIproBatt project at the level of analytics services. It uses the process and sensor data of the plant provided on the semantic level. The data is first analyzed using machine learning and AI methods, and the methods developed for this purpose are combined in an AI toolbox. From a process model, the non-data (physical) part of the hybrid model is defined.

The project implementation takes place in the Laboratory for Medical Informatics and Simulation.

The aim of the KIProBatt research is to reduce reject rate in battery cell production thus making the production processes more efficient. The project thus contributes to increasing sustainability in terms of energy and material consumption. The results are to be commercially utilized in collaborations with industrial companies, especially in the field of battery cell production, and for application in the Münster Research Factory.