AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Railway Sciences Article
PDF (5.3 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research paper | Open Access

Research on the cooperative train control method in the metro system for energy saving

Siyao Li1, Bo Yuan2, Yun Bai3, Jianfeng Liu2( )
Railway Science and Technology Research and Development Center, China Academy of Railway Sciences Corporation Limited, Beijing, China
Transporatation Research Center, Beijing Urban Construction Design & Research Institute Co., Ltd, Beijing, China
School of Traffic and Transportation, Beijing Jiaotong University, Beijing, China
Show Author Information

Abstract

Purpose

To address the problem that the current train operation mode that train selects one of several offline pre-generated control schemes before the departure and operates following the scheme after the departure, energy-saving performance of the whole metro system cannot be guaranteed.

Design/methodology/approach

A cooperative train control framework is formulated to regulate a novel train operation mode. The classic train four-phase control strategy is improved for generating specific energy-efficient control schemes for each train. An improved brute force (BF) algorithm with a two-layer searching idea is designed to solve the optimisation model of energy-efficient train control schemes.

Findings

Case studies on the actual metro line in Guangzhou, China verify the effectiveness of the proposed train control methods compared with four-phase control strategy under different kinds of train operation scenarios and calculation parameters. The verification on the computation efficiency as well as accuracy of the proposed algorithm indicates that it meets the requirement of online optimisation.

Originality/value

Most existing studies optimised energy-efficient train timetable or train control strategies through an offline process, which has a defect in coping with the disturbance or delays effectively and promptly during real-time train operation. This paper studies an online optimisation of cooperative train control based on the rolling optimisation idea, where energy-efficient train operation can be realised once train running time is determined, thus mitigating the impact of unpredictable operation situations on the energy-saving performance of trains.

Keywords

Energy savingCooperative controlTrain operation schemeMetro system

References

 

Bai, Y., Cao, Y., Yu, Z., Ho, T. K., Roberts, C., & Mao, B. (2019). Cooperative control of metro trains to minimize net energy consumption. IEEE Transactions on Intelligent Transportation Systems, 21(5), 2063–2077.
Crossref Google Scholar
 

Bai, Y., Yuan, B., Li, J., Zhou, Y., & Feng, X. (2020). Cooperative control strategy for energy saving operation of metro train based on rolling optimization. China Railway Sciences, 41(3), 163–170.
Google Scholar
 

Chen, Z., Mao, B., Bai, Y., Chen, S., & Shi, R. (2017). Optimization on energy-efficient operations for trailing train in urban rail system with fixed run-time. Journal of the China Railway Society, 39(8), 10–17.
Google Scholar
 

Howlett, P., & Pudney, P. (1995). Energy-efficient train control. London: Springer.
Crossref
 
Jin, B., Feng, X., Sun, P., Chen, M., & Chai, Y. (2019). Energy saving in metro transit substation through train trajectory optimization. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 2829–2834).
Crossref
 
Lee, D. H., Milroy, I. P., & Tyler, K. (1982). Application of Pontryagin’s maximum principle to the semi-automatic control of rail vehicles. In Second Conference on Control Engineering 1982: Merging of Technology and Theory to Solve Industrial Automation Problems. Institution of Engineers.
 
Li, K. (2014). The study on energy saving optimization method of multi-train cooperative control. Beijing Jiaotong University.
 

Liu, J., & Zhao, N. (2017). Research on energy-saving operation strategy for multiple trains on the urban subway line. Energies, 10(12), 2156.
Crossref Google Scholar
 

Liu, J., Guo, H., & Yu, Y. (2016). Research on the cooperative train control strategy to reduce energy consumption. IEEE Transactions on Intelligent Transportation Systems, 18(5), 1134–1142.
Crossref Google Scholar
 

Ran, X., Chen, S., Bai, Y., Chen, Y., Chen, Z., & Yang, C. (2022). Train energy-efficient and passenger time-saving timetable optimization model for urban rail transit line with tidal passenger flow. China Railway Sciences, 43(1), 171–181.
Google Scholar
 
Sun, X., Cai, H., Hou, X., Zhang, M., & Dong, H. (2014). Regenerative braking energy utilization by multi train cooperation. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2014 (pp. 139–144).
 

Sun, X., Lu, H., & Dong, H. (2017). Energy-efficient train control by multi-train dynamic cooperation. IEEE Transactions on Intelligent Transportation Systems, 18(11), 3114–3121.
Crossref Google Scholar
 

Tang, H., Wang, Q., & Feng, X. (2015). Energy saving control of metro train tracing operation. Journal of the China Railway Society, 37(1), 37–43.
Google Scholar
 
Urban Rail Transit (2022). Annual Statistics and analysis Report. China Association of Metros. Available from: https://www.camet.org.cn/tjxx/11944 (accessed 31 March 2023).
 

Xun, J., Tang, T., Song, X., Wang, B., & Jia, Z. (2015). Comprehensive model for energy-saving train operation of urban mass transit under regenerative brake. China Railway Sciences, 36(1), 104–110.
Google Scholar
 

Zhao, N., Roberts, C., & Hillmansen, S. (2014). The application of an enhanced brute force algorithm to minimise energy costs and train delays for differing railway train control systems. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 228(2), 158–168.
Crossref Google Scholar
 

Zhao, N., Roberts, C., Hillmansen, S., & Nicholson, G. (2015). A multiple train trajectory optimization to minimize energy consumption and delay. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2363–2372.
Crossref Google Scholar
 

Zhou, Y., Bai, Y., Li, J., Mao, B., & Li, T. (2018). Integrated optimization on train control and timetable to minimize net energy consumption of metro lines. Journal of Advanced Transportation, 2018, 1–19. doi: 10.1155/2018/7905820.
Crossref Google Scholar
Railway Sciences
Volume 2 Issue 3,
October 2023
Pages 371-394
DOI: 10.1108/RS-07-2023-0025
Cite this article:
Li S, Yuan B, Bai Y, et al. Research on the cooperative train control method in the metro system for energy saving. Railway Sciences, 2023, 2(3): 371-394. https://doi.org/10.1108/RS-07-2023-0025

151

Views

1

Downloads

1

Crossref

Altmetrics
Received: 14 July 2023
Revised: 12 August 2023
Accepted: 16 August 2023
Published: 13 September 2023
© Siyao Li, Bo Yuan, Yun Bai and Jianfeng Liu. Published in Railway Sciences.

This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
Return