Energy-Saving Applications Based on Train Mass Online Learning Using Time-Varying Train Model

The current train model of the train control system is unable to accurately reflect the influence of nonlinear running resistance, line conditions, the mutative train mass value, and external environment changes on the model in train dynamics, resulting in a defect of poor train model performance. T...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) 2022-06, Vol.15 (12), p.4378
Main Authors: Wang, Jie, Xiao, Jin, Hu, Xiaoguang
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
China
Control algorithms
Control systems
curve resistances
Data mining
Distance learning
Energy conservation
Energy consumption
Energy management systems
Energy use
Genetic algorithms
High speed rail
Learning
movement resistances
neural network
Neural networks
Online education
online learning
Optimization
rail transit
train modeling
Trains
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The current train model of the train control system is unable to accurately reflect the influence of nonlinear running resistance, line conditions, the mutative train mass value, and external environment changes on the model in train dynamics, resulting in a defect of poor train model performance. The train basic model and additional resistances are discussed in this paper, a novel neural network online learning method of the time-varying dynamic train model is proposed, combined with the characteristics of rail transit lines, and a neural network learning algorithm is designed by categories and steps. This method can identify the train mass value that changes continuously with passengers during running. The energy savings resulting from using the actual varying train mass in the train control system are calculated. The results show that, when compared to the traditional model’s invariant approximate empirical parameters, the time-varying parameter model can follow changes in the train and line environment and obtain quantitative expressions of curve resistance and tunnel resistance with speed. The time-varying train model was used to conduct engineering tests on the Beijing Capital Airport Line; the online learning deviation of train mass was controlled within a margin of 3.08%, and at the same time, energy consumption decreased by 6.13%.
ISSN:1996-1073
1996-1073
DOI:10.3390/en15124378