Structure

3D additive manufacturing and new manufacturing technologies

The main objective of this work package is to improve the understanding of how additive manufacturing (AM) technologies can contribute to Traction drive system development and manufacturing by reducing conception lead- time, prototype manufacturing cost and improving performance and reliability, thus reducing overall maintenance cost, for the part and the global system.

This study will be based on the development, the prototyping and the test of heat exchangers optimized thanks to additive manufacturing.

To improve the expected benefits, the exchanger design will be optimized through the development of a Multidisciplinary Design Optimisation method that will take into account appropriate physics and AM processes characteristics.

Wireless Dynamic Charging for urban vehicles based on SiC semi-conductors

The objective of this work package is the design of a highly efficient wireless dynamic charging system based on SiC devices. The impact of a dynamic wireless infrastructure on actual city profiles will be evaluated by studying the reduction on the on-board ESS. Moreover, the real benefits of SiC devices will be evaluated to correctly size the improvements expecting at higher switching frequencies for higher transfer distances. Finally, the information gathered in the first two tasks will be used to size a dynamic wireless system for an actual city profile, and the critical concepts will be evaluated in a small-scale prototype.

Investigations on reliability of traction components and lifetime mechanisms

The objectives are to receive basic data and define validated lifetime models for power semiconductor devices, especially novel SiC modules, based on the measurements performed in this work package. The main part of WP3 are the reliability and lifetime tests on SiC MOSFET modules. Their adoption introduces new reliability risks, which must be well understood and quantified to avoid increased failure rates in field service when applied to rolling stock with its demanding mission profiles.

Big Data, Artificial Intelligence (AI) applied to Traction systems smart and predictive maintenance

This WP aims at reaching the following objectives:  Predictive Maintenance (PdM) for train traction systems (identification of opportunities and needs),

Knowledge, Information and Data (KID) available for train traction systems (selection of proper AI methods for their usage for PdM); development of AI methods to exploit Big Data from train traction systems.

Dissemination, Exploitation and knowledge transfer

The main objective of this work package is to ensure that the project results & outputs are disseminated widely and effectively exploited by their target groups (including S2R).

Project Management and Technical Coordination

The objectives of this work package are to ensure effective coordination of the project, including technical, administrative and financial management.