Project runtime: 01.06.2023 – 31.05.2026
2023-09-22_Vollversammlung_Poster_DaMaStE
2024-02-29_BMBF_KickOff_DaMaStE
2024-09-02_Projektübersicht_DaMaStE
2024-09-19_Vollversammlung_DaMaStE
The BMBF project DaMaStE explores new digital ways to improve data-based collaboration in the development of lithium-ion batteries. Experts from raw material and cell production are working with scientists on the use of conductive, carbon-based additives. Together they generate and exchange data on these raw materials and research the connection between raw material properties and production and product characteristics. The interaction of the various material components of a battery electrode during production and operation is complex. Identifying these relationships is costly and time-consuming. Raw material and cell production as well as research generate extensive data that can often only be shared to a limited extent due to incompatibilities and confidentiality interests. The DaMaStE project aims to improve data exchange and digital collaboration in battery research. To this end, DaMaSte uses a system from the DigiBatMat project of the BMBF's MaterialDigital platform. On this basis, a federated approach is being researched that protects data and yet enables its exchange on the largest possible scale. Data is automatically transferred from production processes to the platform and compared with material data to understand the effect of variations in raw material qualities and the impact of new types of raw materials. New types of carbon additives from the industrial partner Northern Graphite are being tested. Likewise, modified carbon additives from INM are being investigated to be used in the cathodes of the partner UniverCell. Aalen University is researching the distribution of carbon in the electrode materials with the help of electron microscopy. Material models from the Leibniz Institute for New Materials gGmbH are used for evaluation. The data thus obtained is brought together and linked to ontologies that establish relationships between raw materials, processes, material structure and properties. The August-Wilhelm Scheer Institute is taking on the development of the system and the machine learning components as a digitalisation partner and project coordinator.
