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Battery Cell Assembly Twin

Project description

A Digital Twin solution for battery manufacturing

The global demand for advanced batteries is surging, driven by the electric vehicle revolution and renewable energy storage needs. However, traditional manufacturing methods struggle to keep pace, facing challenges in efficiency, quality, and trustworthiness. In this context, the EU-funded BatCAT project is pioneering a transformative solution. Specifically, it is aligning with the BATTERY 2030+ Roadmap to construct a Digital Twin for battery manufacturing, merging data-driven and physics-based methods. It tackles the triple challenge of design, operation, and trust, enhancing product quality and process efficiency. Through its interpretable industrial decision support system and real-time data analysis, BatCAT empowers decision-makers in Industry 5.0 environments. Crucially, its rigorous approach ensures trustworthy models, fostering transparency and reliability.

Objective

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.

Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.

HORIZON-RIA - HORIZON Research and Innovation Actions

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) HORIZON-CL5-2023-D2-01

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Coordinator

NORGES MILJO-OG BIOVITENSKAPLIGE UNIVERSITET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 864 246,00
Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 864 246,00

Participants (14)

Partners (3)

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