Li, W, Zhao, S, Li, K orcid.org/0000-0001-6657-0522 et al. (3 more authors) (2021) Energy-Efficient Operation Curve Optimization for High-Speed Train Based on GSO Algorithm. In: Recent Advances in Sustainable Energy and Intelligent Systems: 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Han. 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, 30 Oct - 01 Nov 2021, Hangzhou, China. Springer, Singapore , pp. 120-132. ISBN 978-981-16-7209-5
Abstract
The demand for high-speed automatic train operation (ATO) system brings new opportunities and challenges for the high-speed railway field. The requirements of energy consumption, punctuality, safety and smoothness are also increasing, especially the research on energy saving of high-speed trains ushers in a broader development space. This paper proposes quantitative functions of four energy-saving performance indexes and the multi-objective optimization model of train operation curve considering the characteristics of high-speed train ATO system. An energy-saving optimization method of train operation curve based on Glowworm Swarm Optimization (GSO) algorithm is proposed. The simulation results show that the train operation using the high-speed train operation curve optimization method based on the GSO algorithm can save about 16.9% of electrical energy consumption per kilometer compared with that by other optimization algorithms, which verifies the effectiveness of the method. The result from this paper provides theoretical support for practical application.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Singapore Pte Ltd. 2021. This is an author produced version of a conference paper published in Recent Advances in Sustainable Energy and Intelligent Systems: 7th International Conference on Life System Modeling and Simulation, LSMS 2021 and 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021, Hangzhou, China, October 22–24, 2021, Proceedings, Part II. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | High-speed train; Energy-efficient operation; Train operation curve; Multi-objective optimization; GSO algorithm |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Oct 2021 13:15 |
Last Modified: | 17 Oct 2022 00:21 |
Status: | Published |
Publisher: | Springer, Singapore |
Identification Number: | 10.1007/978-981-16-7210-1_12 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:179569 |