Specific Objectives

Workstream 1 – 3D additive manufacturing and new manufacturing technologies
The main global objective is to explore the benefits 3D printing technologies can bring to traction systems, such as:

  • Improve performances and reliability of overall traction drive and power chain. Reducing volume and/or weight of parts, while increasing the part performance, will lead to more stable and controlled part behaviour, increasing its reliability, lifetime and thus reducing overall maintenance cost, for the part and the global system.
  • Reduce prototyping conception lead-time and cost
  • Contribute to the reduction of serial parts manufacturing & maintenance costs

Workstream 2 - Wireless Dynamic Charging for urban vehicles based on SiC semi-conductors
The main objective is to gather all the necessary data to have enough criteria to give a clear assessment of the WPT Opportunistic Charging for specific routes. In addition:
  • Investigate the actual efficiency of future SiC semiconductors in a WPT Charging system
  • Establish the most appropriate topology for each charging category, along with the sizing and rating of all the components
Finally, to achieve a competitive charging solution, a communication free solution while charging is preferable. Therefore, the most effective strategies to reach that goal will be studied and experimentally verified in a small-scale prototype.

Workstream 3 – Investigations on reliability of traction components and lifetime mechanisms
The superordinate objective of this work package is to reduce the threshold for railway rolling stock manufacturers to enter the silicon carbide (SiC) technology.
The main objective is a better understanding of the robustness and reliability of those devices, compared to those of silicon-based converters. This also includes:
  • To define appropriate test procedures and to execute these tests as independent laboratories
  • To compile the measured data together with literature data, simulations and manufacturer information into lifetime models for the high voltage SiC power modules

Workstream 4 – Big Data, Artificial Intelligence (AI) applied to Traction systems smart and predictive maintenance
The main objective of this work package is the exploitation of smart maintenance management systems, based on Machine Learning and Artificial Intelligence (AI) techniques, which will allow:
  • Minimizing downtime by reducing unforeseen failures and false alarms
  • Increasing components, machines and train system operating life
  • Reducing operation and maintenance costs
  • Increasing train punctuality
An additional objective is the development of models for a systematic and rationale decision making on the selection of the AI algorithms to solve the Prognostics and Health Management (PHM).
Finally, the developed algorithms need to be adjusted on the basis of the Knowledge, Information and Data (KID) that incrementally become available during traction system operation.

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