SOURCE-LINKED INTELLIGENCE

e-powerTrain prEdictive mAintenance using physics inforMed learnING

CORDIS · observation · Publication date unknown

y will estimate its degradation, anticipate failures, and ease reparability thus extending its lifespan. With significant improvement of sensors, complex modelling and data processing methods such as Artificial Intelligence (AI), predictive maintenance (PdM) has gained a lot of interest in different fields. Development of PdM methods for the sub-components of the EV powertrain (battery, fuel cell, e-motor, power electronics) is at the heart of TEAMING. Thanks to international staff exchanges, TEAMING will significantly improve the different facets of the PdM solution: sensors, modelling, Digital Twins, adapted AI, and Physics-Informed Machine Learning methods are at the centre of the studies and present a major potential in term of innovation. TEAMING will advance PdM system to better diagnose the internal physical phenomena of the different EV powertrain components and optimise their performance, lifetime, safety, and reliability.” predictive maintenance, powertrain, battery, fuel cell, e-motor, electrical motor, sensors, electrical vehicle, life cycle assessment, LCA, physics-infor

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recordType

award

status

SIGNED

region

EU

value

1283400

unit

EUR