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Active HORIZON European Commission

Battery Cell Assembly Twin

€5.11M EUR

Funder European Commission
Recipient Organization Norges Miljo-Og Biovitenskaplige Universitet
Country Norway
Start Date Jan 01, 2024
End Date Jun 30, 2027
Duration 1,276 days
Number of Grantees 18
Roles Participant; Associated Partner; Coordinator
Data Source European Commission
Grant ID 101137725
Grant Description

BatCAT is the project that realizes the manufacturability programme from the BATTERY 2030+ Roadmap, creating a digital twin for battery manufacturing that integrates data-driven and physics-based methods.

It develops a cross-chemistry data space for two technologies, (1) Li-ion and Na-ion coin cells and (2) redox flow batteries, addressing a triple challenge in digital manufacturing: (i) Design, (ii) operation, and (iii) trust.(i) By improved product and process design and optimization, product quality and process efficiency increase.

This requires decision support that makes complex decision problems accessible to human decision makers.

The digital twin technology from BatCAT provides an interpretable industrial decision support system (IIDSS) based on multicriteria optimization.

Surrogate modelling connects the high-level analysis firmly to ground-truth data.(ii) Process operation and control is improved by acquiring and analysing sensory and operando data at real time, facilitating live interventions within an Industry 5.0 real-time environment.

BatCAT follows a rigorous approach to actionable modelling, combining data-driven methods with deductive reasoning based on ontologies and formal methods (answer set programming and BPMN-based model checking) to guarantee a reliable behaviour.(iii) The approach from BatCAT produces trustworthy models: Machine learning always retains a clearly characterized connection to the ground truth, and any decision support or decision making from inductive reasoning is safeguarded by constraints through formal deductive reasoning.

All our models and methods are explainable, and all our data are FAIR and explainable-AI-ready (XAIR).The digital twin is validated in pilot production lines for (1) coin cells and (2) redox flow batteries, proving its transferability across chemistries.

The project is closely connected to the Advanced Materials 2030 Initiative, BIG-MAP and BATTERY 2030+, BEPA, DigiPass CSA, EOSC, EMMC, and the Knowledge Graph Alliance, ensuring a community and industry uptake of the results.

All Grantees

Fundacion Universidad Loyola Andalucia; Goldbeck Consulting Limited; Centre for Process Innovation Limited Lbg; Hochschule Kaiserslautern; Fraunhofer Gesellschaft Zur Forderung Der Angewandten Forschung Ev; Indiscale Gmbh; Simula Research Laboratory As; Kemijski Institut; Luxembourg Institute of Science and Technology; Norges Miljo-Og Biovitenskaplige Universitet; United Kingdom Research and Innovation; Rheinland-Pfalzische Technische Universitat; Politecnico Di Torino; Danmarks Tekniske Universitet; Bi-Rex- Big Data Innovation Research Excellence; Universitaet Klagenfurt; Ifp Energies Nouvelles; Vanevo Gmbh

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