E-powertrain predictive maintenance using physics informed learning (TEAMING)

Research Project, 2023 – 2027

Mobility electrification plays a critical role in the decarbonisation of the economy, and we are on the edge of an industrial revolution linked to the massive deployment of the electric vehicle (EV). Their technology readiness level has significantly increased, and the EV can now replace the thermal vehicle in terms of service provided, supporting the EU decarbonisation effort. Besides the reduction of critical material, and decrease of cost, optimizing the lifetime of the EV components is essential to ease their adoption, especially the powertrain sub-components that have the major impact on EV cost and CO2 emissions.A new generation of diagnostic and prognostic systems for the powertrain will be a game changer to ensure EV adoption, because they will estimate its degradation, anticipate failures, and ease reparability thus extending its lifespan. With significant improvement of sensors, complex modeling 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  this project TEAMING. Thanks to Marie Skłodowska-Curie Actions Staff Exchanges, TEAMING will significantly improve the different facets of the PdM solution.