BatCAT: Battery Cell Assembly Twin
BatCAT aims to create a digital twin for multiphysics and multiscale simulations integrating both data-driven and physics-based models for battery manufacturing. Simulation methods range from the electronic through atomistic, mesoscopic, and continuum levels up to technical and business process modelling, permitting the generation of quantitatively reliable data-driven surrogate and actionable models. Data is managed through a federated knowledge base with CaosDB as its central component, adhering to the FAIR principles and employing semantic web technology based on a system of EMMO-aligned ontologies, compliant with the series of CEN Workshop Agreements endorsed by the European Materials Modelling Council community.
The digital twin technology from BatCAT comprises an interpretable industrial decision support system based on multicriteria optimization and reasoning techniques over description logic and first-order logic, including answer set programming and BPMN-based model checking, and a real-time environment for deployment within the production line.
Both coin cell manufacturing and redox flow battery manufacturing are addressed, and simulations are validated through pilot production lines. Transferability across explored chemistries is validated for both use cases. Inductive reasoning from machine learning by cellular neural networks is combined with logics-based deductive reasoning, ensuring that all decision support and decision making facilitated by the digital twin technology is explainable and quantitatively robust at all times, permitting the use of BatCAT for both business and security relevant aspects of Industry 5.0 environments in battery manufacturing.
The project is coordinated by the Norwegian University of Life Sciences (NMBU). It is closely connected to BIG-MAP and the BATTERY 2030+ CSA3, digital marketplaces for Industry Commons (VIMMP, MarketPlace, DOME4.0), EOSC and NFDI-related projects and working groups, and the OntoCommons CSA, ensuring a community and industry uptake of the results.
Funding source
This project is funded by Horizon Europe under the call Cross-sectoral solutions for the climate transition (HORIZON-CL5-2023-D2-01).
Disclaimer: Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or CINEA. Neither the European Union nor the Granting authority can be held responsible for them.
Partners
- NMBU (Norway), coordinator
- Fraunhofer Institute for Industrial Mathematics ITWM (Germany)
- Kemijski Institut (Slovenia)
- Politecnico di Torino (Italy)
- Rheinland-Pfalzische Technische Universitat (Germany)
- IFP Energies Nouvelles (France)
- Universitaet Klagenfurt (Austria)
- Danmarks Tekniske Universitet (Denmark)
- IndiScale GmbH (Germany)
- Fundacion Universidad Loyola Andalucia (Spain)
- Hochschule Kaiserslautern (Germany)
- Simula Research Laboratory AS (Norway)
- BI-REX - Big Data Innovation Research Excellence (Italy)
- Vanevo GmbH (Germany)
- Luxembourg Institute of Science and Technology (Luxembourg)
- United Kingdom Research and Innovation (United Kingdom)
- Centre for Process Innovation Limited LBG (United Kingdom)
- Goldbeck Consulting Limited (United Kingdom)