SOURCE-LINKED INTELLIGENCE

BATTERY MATERIAL CHARACTERISATION AND DIGITAL TWINS FOR CELL TO PACK PERFORMANCE IN AGILE MANUFACTURING PILOT LINES AND AUTOMOTIVE FIELD

CORDIS · observation · Publication date unknown

gy materials. It is based on a toolset of innovative and state-of-the-art characterisation methods for multiscale materials, interoperable tests, and analytical models supported by and linked through machine learning. With this, the production costs, materials waste, and the CO2 footprint in production lines will be reduced, while in parallel the battery electrochemical performance at the single cell level will be increased. The new measurement tools and multi-scale modelling algorithms lead to a higher characterisation speed (factor of 5) and an improved accuracy in cell tests by an order of magnitude, as will be demonstrated on the lab bench and in pilot lines. DigiCell develops a new holistic approach for open-source algorithms and data standardization strategies; new quality assessments for a healthy, safe, and circular economy. The project readily interfaces and interacts tightly with EMMC. Battery test, digital twin, modelling, pilot line, Green Deal, data accuracy, machine learning, lithium-ion

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recordType

award

status

SIGNED

region

EU

value

5405098

unit

EUR