The international practices in organizing the energy consumption control of electric rolling stock are analyzed. As a result, it was concluded that currently the issue of organizing the energy consumption control of electric rolling stock is mainly solved by using analytical methods. These methods are based on designing the simulation models, which are usually based on the Pontryagin maximum principle. However, considering the development of recording systems for motion parameters of electric rolling stock, as well as other automated systems of Russian Railways, it seems promising to develop and study artificial intelligence methods and algorithms for solving real-time monitoring issues of electric rolling stock energy consumption. It was also determined that the most modern motion parameter recorders have a number of significant drawbacks from the data analysis point of view. Such drawbacks include insufficient data and their low reliability, lack of linking the recorded data to trips and locomotive teams, the impossibility of choosing a constant interval for recording measurement results. Moreover, there is also high probability of errors when recording data on the cartridge, lack of GPS/GLONASS satellite navigation system, lack of wireless data transmission, imperfection of software and inconvenience of exporting data from a cartridge file and its incompleteness. In order to test the energy efficiency assessment of electric rolling stock within the limits of arbitrary energy tracking areas, the Corresponding software was developed on the basis of data from the motion parameters recorders. However, developing the new complex automated system is required for the full implementation of the proposed consumption tracking method. Such system should combine the entire set of measured parameters, both for electric rolling stock and for the traction power supply system.
Artificial intelligence methods to control the energy efficiency of electric rolling stock online
