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Cost-efficient and reliable trains, including high-capacity trains and high-speed trains

IP Coordinator: Javier Goikoetxea - CAF


Total Project Value:
€ 2 300 036,25
from 01/12/2020 to 30/09/2023
S2R (Of H2020) co-funding:
€ 2 300 036,25
Marta Garcia
Complementary projects:
Project website:


Rail is a fundamental service for modern societies and the backbone of a sustainable transport system, capable of serving the growing demand of both passenger and freight. To meet the numerous challenges ahead (demographic development, climate change, etc.) the global rail sector must increasingly rely on the emerging disruptive technologies such as advanced robotics, 3-D printing, high computing power and connectivity, etc., which are integrated with analytical and cognitive technologies that enable machine-to-machine (M2M) and machine-to-human (M2H) communication. In addition, comes the pressure to reduce energy consumption, pollution and the consumption of other resources. Therefore, mastering the breakthrough developments of new technologies is of capital importance for the railway industry to deliver smart and efficient solutions that improve safety, security, punctuality, availability, accessibility, seamless operation, capacity, connectivity, sustainability and other performances, while remaining economically affordable for everybody in countries all around the world. Indeed, essential to the growth of the rail industry is the reduction of the overall life cycle exploitation costs of all rail sub-systems, the minimisation of the effects of obsolescence and the effective migration of emerging technological innovation.

Among the new technologies, the present project focuses on the following ones, which are particularly relevant for train traction systems:

  • Development of design approaches, end-to-end conception time evaluation and feasibility/performance study of 3D printing technologies for new traction system components use cases. Additive Manufacturing (AM) technologies open a disruptive way to design and manufacture parts or components for many industrial applications due to the complex and disruptive design, functions and components integration, on-demand materials properties, etc. With the potential in terms of cost and lead-time reduction for prototyping, parts performance improvement and weight reduction, AM of spare parts represents a significant opportunity.
However, taking into account the current state of the art, the expected gain currently depends on a high number of parameters:
-  Discrepancies in terms of process reliability, robustness, maintenance costs, etc. Many AM technologies are still under development and R&D activities are required to support their qualification for industrial integration
-  Process limitations in terms of parts size, deposition rate, compatibility with a specific material
-  The range of machine cost is quite significant and can be a strong showstopper for industrial business case.

The objective is to contribute to improve the understanding of AM technologies benefits for prototyping and manufacturing of train power traction systems parts through a down selection and assessment of an appropriate use case and the development of required Multidisciplinary Design Optimisation methods to take into account AM processes opportunities and limitation.

  • Although Wireless Power Transfer (WPT) is an interesting alternative compared to current charging systems, its sizing for specific routes and global efficiency of the system are still a challenge to be faced to reach a near future implementation of WPT in railway applications. A main challenge associated with Dynamic Wireless Power Transfer (DWPT) is the short time period in which the transmitting and receiving pad can interact with each other. Moreover, with DWPT other concerns like magnetic field exposure have arisen. Thus, the objective is to size an opportunistic charging system considering the actual on-board Energy Storage Systems (ESS), evaluate experimentally the benefits of SiC based semiconductors, and size the system not only considering efficiency, transferred power or installed weight targets but also assuring no field exposure is present for a save employment of the technology.
  • A significant contributor to rolling stock failures is the traction converter and, in particular, its power semiconductor devices. New devices like high voltage SiC modules can increase the converter efficiency considerably, but must not affect the reliability and availability of the rolling stock adversely. For this reason, understanding the limits of robustness and reliability of those components is of paramount importance for the design of railway converters. Thus, one of the main objectives is improving the understanding of the robustness and reliability of high voltage SiC modules with respect to railways traction applications and their particular requirements.
  • New know-how generated by the project is also of significance and actuality for electrical propulsion in other applications and for development of power electronic energy conversion characterized by high energy efficiency and low environmental footprint.
  • Development of smart maintenance approaches enabled by predictive analytics, trained on big data. This is expected to reduce the system life cycle costs and increase its availability, while still guaranteeing the safety.
In order to achieve these high-level objectives, RECET4Rail will implement a coordinated set of research activities addressing in four technical Work Packages (WP). The four workstreams envisaged by the call are: 3D additive manufacturing and new manufacturing technologies; Wireless Dynamic Charging for urban vehicles based on SiC semi-conductors; Investigations on reliability of traction components and lifetime mechanisms; and Big Data, Artificial Intelligence (AI) applied to Traction systems smart and predictive maintenance.

Results and Publications

D1.1 States of the art review & KPI


D1.6 Executive Summary of D1.2 - Preliminary feasibility study & performance benchmark


* D1.7 Executive Summary of D1.4 - Demonstrators experimental and simulation results analysis


D2.3 Design of a full scale WPT architecture for actual city profiles


D3.2 Separation and modelling of different threshold voltage


D3.5 Assessment of cosmic ray capability


D4.1. Definition of quantitative metrics for assessing the performance of PdM for train traction


D4.2 PdM economic KPI: quantitative modelling and analysis


D4.4 Decision method to select the best AI analytics


D4.5. Development of Transfer Learning algorithms


D4.6 Development of PHM algorithms


* Please note that this/these deliverable(s) is/are undergoing S2R JU review and acceptance processes.

All deliverables, results and publications herewith provided reflects only the author's view and the S2R JU is not responsible for any use that may be made of the information it contains.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No: 101015423