Updated at: 31-07-2023
Type: / /
Updated at: 18-02-2023
Project: ConnectedFactories 2
Updated at: 20-12-2022
Updated at: 05-12-2022
There seems to be relationship to predict torque with use of in-line data. Needs to be more explored
An AI vision algorithm developed by TNO (WP3) seems to filter bad rated parts compared to installed algorithm. Advantage can be when product print is changing to catch-up development speed in traditional algorithm development.
Project: Fortissimo 2
Updated at: 03-10-2022
Cloud level: Cygnus extension
Updated at: 09-08-2022
Operational services aim to collect product data on post-use Li-Ion batteries about their use phase in order to enable monitoring and full traceability of its life-cycle;
Operational services aim to:
- elaborate and analyse various data collected about post-use batteries to predict the conditions of the battery packs, modules and cells;
- define a Decision Support System to identify the best disassembly and remanufacturing strategy, given the post-use Li-Ion battery conditions.
As the proactive exploitation of the DigiPrime platform enables the car-monitored SOH tracing and availability, less testing is needed to assess the residual capacity of the battery. Moreover, by knowing the structure of the battery packs, a decision support system can be implemented to adjust the de-and remanufacturing strategy accordingly and select the most proper cells for re-assembly second-life modules, thus unlocking a systematic circular value chain for Li-ion battery cells re-use. Furthermore, excessively degraded cells which cannot be re-used can be sent to high-value recycling, based on the knowledge of their material compositions.