Pouls, Kevin BernardKevin BernardPoulsHülsmann, Tom-HendrikTom-HendrikHülsmann2025-07-182025-07-182025https://publica.fraunhofer.de/handle/publica/48969710.1016/j.procir.2025.03.0352-s2.0-105009398925Battery technology plays a significant role in the global energy transition and the transformation of the transport sector. However, the industry continues to be challenged by long ramp-up times, low yields and high scrap rates, while generating large amounts of data that remain largely unused. Reasons for this include the complex and interlinked process chain of battery cell production, the lack of public knowledge (e.g. regarding cause-efect relationships) in the industry, and complex data architectures, in which relevant data is stored in numerous systems and databases. Graph-based approaches for data modeling, storage and access can help to address these challenges by linking and storing data and information (e.g. known cause-efect relationships) in a machine and human-readable way. In this paper, two proofs of concept were developed to demonstrate the application of graph-based systems and data structures in battery cell manufacturing. Initial results indicate that these approaches are well suited to model the types of data and relationships commonly found in battery cell production.enfalsebattery cell manufacturingdata engineeringGraphQLgraphsknowledge-graphontologiesjournal article