The article formulates the relevance of the development of methods for increasing the efficiency of using mainline freight electric locomotives by reducing electricity losses by improving on-board driving systems and decision support through the use of intelligent systems for generating optimal operating modes. As a parameter under study, when solving the problem of predicting the recommended operating mode of an electric locomotive, the setting of the traction force was adopted, since it has a significant effect on the amount of electrical energy consumption, determines the nature of the locomotive movement and is not tied to the design features of a particular series of electric locomotives. To solve this problem, long short-term memory (LSTM) networks were chosen. This network architecture is insensitive to time gaps between events, which allows them to be used in tasks where long-term and delayed effects must be taken into account. This property is achieved by isolating two feedbacks: the hidden state and the cell state, and adding gates to control them. As a result, a tuned artificial recurrent neural network was obtained, which allows calculating the traction force settings for the next 10 s based on the data on the locomotive operation over the last 30 s.
Control Method for Mainline Freight Electric Locomotives with the Possibility of Adjusting Movement Modes
Lect. Notes in Networks, Syst.
International School on Neural Networks, Initiated by IIASS and EMFCSC ; 2022 ; St.Petersburg, Russia February 08, 2022 - February 10, 2022
Networked Control Systems for Connected and Automated Vehicles ; Kapitel : 52 ; 523-531
16.11.2022
9 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Control of Mainline Freight Electric Locomotives with Adjustment of Operating Modes
Springer Verlag | 2022
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|America's first diesel mainline freight locomotives completed
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