HORIZON-CL5-2022-D2-01-09: Physics and data-based battery management for optimised battery utilisation (Batteries Partnership)

Keywords: advanced state estimators | Ageing | batteries | battery | battery management | battery management systems | battery operating range | battery system | battery systems | battery usage | BMS | BRIDGE | computational | computational capability | data | data-based | data-based approach | data-based approaches | degradation models | early detection | End of Life | equivalent-circuit models | FAIR data | FAIR principles | hardware | hardware level | knowledge-driven | lifetime | model | models | next generation | next-generation battery management systems | next-generation BMS | offline | open access | performance | performances | physics | physics approach | physics approaches | physics-based | physics-based battery model | physics-based model | prediction | prediction of failure | prediction of failures | predictive | predictive maintenance | prognosis | reliability | safety | semi-empirical | semi-empirical model | semi-empirical models | software | software and hardware | software and hardware levels | software level | state estimators | state-of-the-art | stationary | stationary application | stationary applications | Towards a competitive European industrial battery value chain for stationary applications and e-mobility | transport | transport application | transport applications | TRL 4 | useful lifetime