Digital Twin and Dataspace Architecture for Vanadium Redox-flow Batteries in View of Requirements for the Digital Materials and Product Passport
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Digital Twin and Dataspace Architecture for Vanadium Redox-flow Batteries in View of Requirements for the Digital Materials and Product Passport Martin Horsch, David Fertig, Amirhossein Aghabarari, Maria Bashir, Dmytro Romanov, Fadi Al Machot, Mathijs Janssen, Eirik Valseth, Andreas Linhart, Jan große Austing, Silvia Chiacchiera, Noel Vizcaino, Michael Seaton, and Ilian Todorov BatCAT is the project that realizes the Battery2030+ manufacturability programme from 2024 to 2027 by developing a data space and digital twin platform; primarily, BatCAT considers vanadium-based redox-flow batteries (VFRBs) as well as Li-ion and Na-ion coin cells. The present contribution summarizes the requirements analysis and initial steps of work done within the project, with a focus on the VRFB use case. It discusses how the digital twin system can meet the requirements for the digital materials and product passport as advanced by the DigiPass CSA project. For this purpose, it presents the pre-existing lines of work and ideas from a spectrum of fields that will be combined within BatCAT, specially in physics-based molecular and multiscale modelling and knowledge representation. Physics-based modelling in BatCAT includes molecular dynamics and Monte Carlo simulation based on classical mechanical pair potentials, using the DL_POLY and ms2 codes; mesoscopic DPD simulations will be carried out using DL_MESO, employing an nDPD potential. The molecular and mesoscopic simulation results will feed into continuum simulations, equivalent-circuit models, and population balance models. For use in production, it is necessary to make all these models explainable-AI-ready. BatCAT will combine three approaches to decision support: First, logical reasoning by answer set programming (ASP); it was recently shown that ASP can increase the efficiency of neural-network surrogate modelling while also ensuring its interpretability. Second, an enterprise architecture based on BPMN, DMN, and CMMN. Third, multicriteria optimization, integrating surrogate models into design of simulation and model parameterization, interoperating with the MolMod Database. Model accuracy and reliability will be documented through epistemic metadata. This work has received funding from the EU's Horizon Europe research and innovation programme under GA no. 101137725 (BatCAT) and 10100819 (DigiPass CSA).
