Optimization on the Dynamic Train Coupling Process in High-Speed Railway

CHENG Fanglin; TANG Tao; SU Shuai; MENG Jun

doi:10.23919/cje.2022.00.189

Volume 32 Issue 5

Sep. 2023

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Article Navigation > Chinese Journal of Electronics > 2023 > 32(5): 1002-1010

CHENG Fanglin, TANG Tao, SU Shuai, et al., “Optimization on the Dynamic Train Coupling Process in High-Speed Railway,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 1002-1010, 2023, doi: 10.23919/cje.2022.00.189

Citation:

CHENG Fanglin, TANG Tao, SU Shuai, et al., “Optimization on the Dynamic Train Coupling Process in High-Speed Railway,” Chinese Journal of Electronics, vol. 32, no. 5, pp. 1002-1010, 2023, doi: 10.23919/cje.2022.00.189

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Optimization on the Dynamic Train Coupling Process in High-Speed Railway

doi: 10.23919/cje.2022.00.189

CHENG Fanglin^{1, 2
,},
TANG Tao^{1, 2
,},
SU Shuai^{1, 2
,},
MENG Jun^3
,

1.
State Key Laboratory of Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
2.
Frontiers Science Center for Smart High-speed Railway System, Beijing Jiaotong University, Beijing 100044, China
3.
China Academy of Railway Science, Beijing 100044, China

Funds: This work was supported by the Talent Fund of Beijing Jiaotong University (2021RC271), the National Natural Science Foundation of China (52172322, U22A2046), the Foundation of China State Railway Group Co., Ltd. (L2021G003, L2022G010), the Beijing Natural Science Foundation (L201004, L191015), and the State Key Laboratory of Rail Traffic Control and Safety (RCS2022ZZ003, RCS2022ZI002)

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Author Bio:
Fanglin CHENG was born in 1996. She received the B.E. degree from Beijing Jiaotong University, Beijing, China in 2019. She received the M.E. degreee with the State Key Laboratory of Rail Traffic Control and Safety in Beijing Jiaotong University, Beijing, China, in 2022. Her current research interests include intelligent control, machine learning, and energy-efficient train operation in railway system. (Email: 19120200@bjtu.edu.cn)

Tao TANG was born in 1963. He received the Ph.D. degree in control theory from the Institute of Automation, Chinese Academy of Sciences, Beijing, China, in 1991. He is currently a Professor and the Director of the State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University. His research interests include communication-based train control, high-speed train control system, and intelligent transportation systems. (Email: ttang@bjtu.edu.cn)

Shuai SU (corresponding author) was born in 1987. He received the Ph.D. degree from Beijing Jiaotong University, Beijing, China in 2016. He is currently working as the Deputy Director with the Frontiers Science Center for Smart High-Speed Railway System, Beijing Jiaotong University. His current research interests include energyefficient operation and control in railway systems, intelligent train control and dispatching. (Email: shuaisu@bjtu.edu.cn)

Jun MENG was born in 1983. He received the M.S. degree from Beijing Jiaotong University, Beijing, China, in 2018. Currently he works as an Associate Researcher in Signal & Communication Research Institute, China Academy of Railway Sciences Corporation Limited. His research interests include intelligent train control and information technology in railway system. (Email: mengjun1983@126.com)
Received Date: 2022-07-02
Accepted Date: 2022-12-03

Available Online: 2023-01-14

Publish Date: 2023-09-05

Abstract

Abstract

This work focuses on the driving strategy optimization problem of a scenario in which two trains come from two branches under virtual coupling, aiming at going through the junction area efficiently. A distance-discrete optimal control model is constructed. The optimization objective is to maximize the trip time during which the two trains operate in coupled state. The line conditions, dynamic properties of the trains and the safety protection constraints are considered. The nonlinear constraints are converted into linear constraints with piecewise affine function and logical variables, and the proposed problem is converted into mixed integer linear programming (MILP) problem which can be solved by existing solvers such as Cplex. Four simulation experiments are conducted to verify the effectiveness of MILP. The dynamic programming (DP) algorithm is used as the benchmark algorithm in the case study. Compared with DP algorithm in small state space, MILP has better performance since it shortens the coupling time. Moreover, the improvement of line capacity of virtual coupling is 35.42% compared with the fixed blocking system.
- Virtual coupling,
- Junction area,
- Mixed integer linear programming (MILP)

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References(26)

References

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