Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control

Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies...

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Bibliographic Details
Published in:Energies (Basel) 2020-10, Vol.13 (20), p.5483
Main Authors: Shang, Fei, Zhan, Jingyuan, Chen, Yangzhou
Format: Article
Language:English
Subjects:
Algorithms
Control algorithms
Cost function
Deviation
distributed
Energy conservation
Energy consumption
Energy efficiency
energy saving
Environmental regulations
Headways
metro line
model predictive control
operational constraints
Optimization
Passengers
Real time
Regulations
Schedules
Traffic congestion
train regulation
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Summary:Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies the energy-saving train regulation problem by utilizing distributed model predictive control (DMPC), which is motivated by the breakthrough of vehicle-based train control (VBTC) technology and the pressing real-time control demand. Firstly, we establish a distributed control framework for train regulation process assuming each train is self-organized and capable to communicate with its preceding train. Then we propose a DMPC algorithm for solving the energy-saving train regulation problem, where each train determines its control input by minimizing a constrained local cost function mainly composed of schedule deviation, headway deviation, and energy consumption. Finally, simulations on train regulation for the Beijing Yizhuang metro line are carried out to demonstrate the effectiveness of the proposed DMPC algorithm, and the results reveal that the proposed algorithm exhibits significantly improved real-time performance without deteriorating the service quality or energy efficiency compared with the centralized MPC method.
ISSN:1996-1073
1996-1073
DOI:10.3390/en13205483