SOURCE-LINKED INTELLIGENCE

A next-generation automated, connected, and standardised process for increased safety, efficiency, and sustainability of Li-ion BATTEry REVERSE logistics

CORDIS · observation · Publication date unknown

based on a safe and more efficient human-robot collaboration, and a more precise and faster Remaining Useful Life assessment of battery modules for 2nd life applications based on acoustic testing and machine learning algorithms. On top of that, BatteReverse will develop a Battery Data Space with standardised labelling and battery passport functionalities to improve battery identification. We will connect the stakeholders through a community platform and analyse the entire RL process by a digital twin (DT) simulation that will optimise profitability of RL circular business models. The innovations will be integrated and demonstrated in an operational environment in two use-cases for end-of-first-life (EoFL) EV batteries - recycling and repurposing – mirrored with the DT simulation. By 2026 we expect these developments to contribute to following outcomes: increase recycling efficiency by 5%, raise share of repurposed EoFL batteries to 10%, reduce risk of severe events in reverse logistics to 1/10.000, successfully simulate two successful RL business models and to have a stakeholder’s co

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recordType

award

status

SIGNED

region

EU

value

4910972

unit

EUR