Laboratoire de Génie Informatique et d’Automatique de l’Artois


The Role of Automatic Control in Intelligent Transportation Systems

Le 28 mai 2024 à 14h00 Salle des séminaires du LGI2A, FSA, Béthune
Vasil DIMITROV Professeur Todor Kableshkov University of Transport, 158 Geo Milev Str., 1574 Sofia, Bulgaria
Le séminaire est présenté en anglais.

An approach for implementing adaptive control on the asynchronous drive of electric vehicles has been developed, which also creates opportunities for rapid and accurate fault detection. The method involves building a PLC and sensors to provide the required information in the EV. The obtained data is compared with pre-recorded characteristics stored in a database. The desired control characteristics of the drive (mechanical, traction, speed, etc.) can also be loaded into the database. Therefore, the digitization of these characteristics must be performed in advance.

Digitizing the characteristics and important operational parameters of asynchronous drives in auxiliary machines and traction drives in electric vehicles allows for a higher degree of automation. This provides remote monitoring and control. The developed methodology can be applied to metro trains, trams, trolleybuses, locomotives, railway rolling stock, etc.

This approach will also help increase the efficiency of transport and optimize energy consumption in various aspects, such as :

  • Improving timetables and optimizing scheduling.
  • Managing driving styles to improve train driving techniques.
  • Developing energy management strategies.
  • Enhancing software settings to increase recovery capabilities, such as reusing braking energy through proper control algorithms and integrating innovative energy storage systems (both stationary and onboard).

The approach also includes :

  • Studying the starting acceleration and regenerative braking deceleration of electric cars in different driving modes.
  • Investigating optimization possibilities for the positioning of electric vehicles, among others.

Therefore, the developed approach creates an opportunity to investigate the role of automatic control in intelligent transport systems.