Today, the properties of battery materials are determined anew at every development step through elaborate series tests. The heterogeneous data obtained in this process has so far been difficult to link with data from the manufacturing process and system characterization in order to compare them with each other and the state of research. This project creates a platform for interoperable management of battery materials data that enables predictions of quality and performance through machine learning and correlation analysis. An established (NMC) and an innovative reference electrode material (LMO/LTO) will be characterized, improved and compared from raw material to cell in the project. The complexity of the “battery” system and its heterogeneous materials is accounted for by an ontology in which the components are clearly described at different scales. In this way, material data and knowledge can be logically stringently linked and queried.