The rapid evolution of battery technologies, driven by the demand for renewable energy storage and electric vehicles, gives rise to manufacturing processes with a high production volume and a substantial potential for optimization. Digital twin technology (DTT) development can therefore yield a high return on investment and a significant societal impact. Digital twins promise to improve critical-to-quality KPIs, reduce scrap rates, and contribute to a circular economy by keeping track of metrics required for the digital product passport. Embedded into dataspaces providing semantic interoperability, DTT can enable autonomous and closed-loop optimization and improve R&D efficiency by design of experiment and design of simulation. This minisymposium builds on experience from the KIproBatt [1] and BatCAT [2] projects. It aims to bring together researchers, engineers, and industry professionals to discuss how DTT can be deployed in battery cell manufacturing to contribute to the green and digital twin transition. Contributions will address the integration of physics-based and data-driven models, including machine-learning based surrogate modelling, for process and product design. Work with a focus on knowledge representation for interoperable dataspaces, modelling specific stages of the manufacturing process, or the connection between models of the manufacturing process, battery testing, and battery manufacturing systems is also welcome. In addition, it will be explored how large language models can enhance DTT. Participants in the minisymposium will gain insights into DTT in battery cell manufacturing, including the role of multiphysics simulation, multicriteria optimization, real-time data integration, and circular raw and advanced materials ecosystems.

[1] Stier S.P., Xu X., Gold L., Möckel M., Ontology-based battery production dataspace and its interweaving with artificial intelligence-empowered data analytics, Energy Technology, Vol. 12 (4), p. 2301305 (doi:10.1002/ente.202301305), 2024.
[2] Horsch M.T., Romanov D., Valseth E., Belouettar S., Córdova López L.E., Glutting J., Janssen M.A., Klein P., Linhart A., Seaton M.A., Sødahl E.D., Vizcaino N., Werth S., Stephan S., Todorov I.T., Chiacchiera S., Al Machot F., Battery manufacturing knowledge infrastructure requirements for multicriteria optimization based decision support in design of simulation, in Proc. SeMatS 2024, to appear (doi:10.5281/zenodo.13132899), 2024.