Research database

Transport contributes by almost 25% to the total GHG emissions in Europe and is the main cause of air pollution in cities. Hence, clear actions towards the decarbonisation of the urban road transport would be largely beneficial since more than 70% of the European citizens live in urban areas. Increasing the use of public transportation is for sure one of the main strategies. Unfortunately, the current status of the public transport infrastructure requires enhancements in order to attract a greater number of users. The integration of reliable models of Urban Traction Electrification Systems (UTESs) and Public Transport Vehicles (PTVs) would allow the improvement of the service by identifying more beneficial configuration of the network, detecting criticalities both in UTESs and PTVs, and taking remedial actions to fix the problems before they occurred according to the predictive maintenance approach. To do so, the quantities that should be monitored in quasi real-time are the positions of the vehicles, their electrical demands, the current flows and voltages in all the network branches and nodes, and the contribution of each substation. The currently adopted UTES models do not grant satisfactory and reliable results. Therefore, to overcome their limitations and foster the energy and digital transition, it is necessary to move towards a more sophisticated approach: The Digital Twin (DT). According to industry 4.0, DT is defined as a real physical imitation on digital model for the purpose of system optimization, monitoring, determining and predicting of future problem by employing Artificial Intelligence (AI), machine learning and software analysis including big data from physical systems. The main objectives of the project are: 1. to identify the barriers to the implementation of Urban Traction Electrification Systems (UTES) Digital Twins (DTs), as well as the strategies to overcome them; 2. to design, develop and test, in the lab and on-site, a prototype of a multifunction low-cost smart meter to monitor the pantograph voltage and current demand, as well as the geographical position of a PTV in UTES characterized by a rated voltage up to 1.5 kV; 3. to design, develop and test an open source, multi-layer, client-server DT model of a UTES; 4. to use the UTES DT model within two research areas, which are the integration of DC-connected generators and loads into UTESs, and the implementation of Predictive Maintenance (PM) algorithms. External support will be provided by two companies for all the activities of the project: Infra.To and GTT, which own and manage the Urban Traction Electrification Systems (UTESs) and the Public Transport Vehicles (PTVs) in Turin, respectively.