Simulation and analysis of optimal energy-saving mode in microfield of underground rail transit

Authors

DOI:

https://doi.org/10.61089/aot2024.hc8s5k97

Keywords:

Underground rail transit, Field theory, Automatic train driving, Energy-saving model

Abstract

Underground rail transit has become one of the most popular transportation modes because of its advantages such as fast running speed, large passenger flow and punctuality. This mode of transportation can alleviate urban traffic congestion to a certain extent, so underground rail transit has been vigorously developed, and the number of rail lines has increased exponentially. However, with the continuous development of underground rail transit, the energy consumption of train operation is also increasing, resulting in a waste of energy. To solve this problem, this paper proposes to improve the energy-saving technology of train operation by using train autopilot control strategy. Firstly, a train operation optimization model considering train position and speed limit is established by using automatic train driving strategy, and the nonlinear problem of the optimization model is solved by genetic algorithm. The micro-field theory is introduced into the control strategy of automatic train driving, and the energy-saving model of underground rail transit is established. The energy consumption is optimized from three aspects: train energy-saving technology, line planning and overall operation planning of rail transit. The simulation results show that the average impact rate and energy consumption of trains under the proposed energy-saving model are significantly reduced compared with the other two groups of trains, and the average impact rate and energy consumption are significantly reduced compared with the other two groups of trains. The running time and energy consumption of the energy-saving model are lower than those of the experimental group. To sum up, the energy-saving model of underground rail transit proposed in this paper not only reduces the operating energy consumption, but also improves the passenger comfort, which can provide low-cost energy-saving technology for underground transportation field, and has positive significance for urban low-carbon development.

References

Ana, C., Todeva, E., Carnauba, A., Boaventura, J., & Pereira, C. (2020) Formal and relational mechanisms of network governance and their relationship with trust: substitutes or complementary in Brazilian real estate transactions. International Journal of Networking and Virtual Organiza-tions, 22(3), 246-271. https://doi.org/10.1504/IJNVO.2020.10027652.

Barma, M., & Modibbo, U. (2022) Multi-objective mathematical optimization model for municipal solid waste management with economic analysis of reuse/recycling recovered waste materials. Journal of Computational and Cognitive Engineering, 1(3), 122-137. https://doi.org/10.47852/bonviewJCCE149145.

Boyacioglu, P., Bevan, A., Allen, P., Bryce, B., & Foulkes, S. (2022) Wheel wear performance assessment and model validation using Harold full scale test rig. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 236(4), 406-417. https://doi.org/10.1177/09544097211022444.

Cai, Y., & Chen, J. (2021) Independent cover is ogeometric Reissner‐Mindlin shell model for the simulation of the fabricated lining. International Journal for Numerical and Analytical Methods in Geomechanics, 45(17), 2490-2521. https://doi.org/10.1002/nag.3274.

Feng, M., Wu, C., Lu, S., & Wang, Y. (2020) Notch-based speed trajectory optimization for high-speed railway automatic train operation. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 236(2), 159-171. https://doi.org/10.1177/09544097211042184.

Hou, B., Zeng, Q., Fei, L., & Li, J. (2020) Noise evaluation method for urban rail transit under-ground station platforms. Journal of Tsinghua University (Science and Technology), 61(1), 57-63. https://doi.org/10.16511/j.cnki.qhdxxb.2020.22.019.

Isik, M., Dodder, R & Kaplan, P, O. (2021) Transportation emissions scenarios for New York City under different carbon intensities of electricity and electric vehicle adoption rates. Nature energy, 6(1): 92-104. http://dx.doi.org/10.1038/s41560-020-00740-2.

Jia, C., Xu, H., & Wang, L. (2020) Robust nonlinear model predictive control for automatic train operation based on constraint tightening strategy. Asian Journal of Control, 24(1), 83-97. https://doi.org/10.1002/asjc.2419.

Kong, L., Wu, Z., Chen, G., Qiu, M., Mumtaz, S., & Podriguse, J. (2020) Crowdsensing based cross-operator switch in rail transit systems. IEEE Transactions on Communications, 68(12), 7938-7947. https://doi.org/10.1109/TCOMM.2020.3019527.

Kargwal, R., Kumar, A., Garg, M, K & Chanakaewsomboon, I. (2022) A review on global energy use patterns in major crop production systems. Environmental Science: Advances, 2022, 1(5): 662-679. http://dx.doi.org/10.1039/d2va00126h.

Mccord, M., Davis, P., Mccord, J., Haran, M., & Davison, K. (2020) An exploratory investigation into the relationship between energy performance certificates and sales price: A polytomous uni-versal model approach. Journal of Financial Management of Property and Construction, 25(2), 247-271. https://doi.org/10.1108/JFMPC-08-2019-0068.

Muniandi, G. (2020) Blockchain-enabled virtual coupling of automatic train operation fitted main-line trains for railway traffic conflict control. IET Intelligent Transport Systems, 14(4), 611-619. https://doi.org/10.1049/iet-its.2019.0694.

Meng, X., Gu, A., Wu, X., Zhou, L., Zhou, J., Liu, B & Mao, Z. (2021) Status quo of China hydro-gen strategy in the field of transportation and international comparisons. International Journal of Hydrogen Energy, 46(57): 28887-28899. http://dx.doi.org/10.1016/j.ijhydene.2020.11.049.

Maheshwari, S., Shetty, S., Ratnakar, R & Sanyal S. (2022) Role of Computational Science in Materials and Systems Design for Sustainable Energy Applications: An Industry Perspective. Jour-nal of the Indian Institute of Science, 2022, 102(1): 11-37. http://dx.doi.org/10.1007/s41745-021-00275-9.

Mironiuk W, Buszman K. (2023) Capabilities to use passive measurement systems to detect objects moving in a water region. Archives of Transport, 2023, 68(4): 137-156. https://doi.org/10.61089/aot2023.bw74g958

Novak H, Lešić V, Vašak M. Energy-efficient model predictive train traction control with incorpo-rated traction system efficiency. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(6): 5044-5055.. https://doi.org/10.1109/TITS.2020.3046416

Rautiainen, M., Remes, H., & Niemel, A. (2021) A traction force approach for fatigue assessment of complex welded structures. Fatigue and Fracture of Engineering Materials and Structures, 44(11), 3056-3076. https://doi.org/10.1111/ffe.13548

Shao, C., Feng, C., Shahidehpour, M., Zhou, Q., Wang, X & Wang, X. (2021) Optimal stochastic operation of integrated electric power and renewable energy with vehicle-based hydrogen energy system. IEEE Transactions on Power Systems, 36(5): 4310-4321. http://dx.doi.org/10.1109/tpwrs.2021.3058561.

Srivastava, D., Kapoor, K & Amarendra, G. (2022) Development of Advanced Nuclear Structural Materials for Sustainable Energy Development. Journal of the Indian Institute of Science, 102(1): 391-404. http://dx.doi.org/10.1007/s41745-022-00287-z.

Tardivo, A., Carrillo Zanuy, A & Sánchez Martín, C. (2021) COVID-19 impact on transport: A paper from the railways’ systems research perspective. Transportation research record, 2675(5): 367-378. http://dx.doi.org/10.1177/0361198121990674.

Vickerstaff, A., Bevan, A., & Boyacioglu, P. (2020) Predictive wheel-rail management in London Underground: Validation and verification. Proceedings of the institution of mechanical engineers, Part F: Journal of Rail and Rapid Transit, 234(4), 393-404. https://doi.org/10.1177/095440971987861

Wang, Q., Xi, H., Deng, F., Cheng, M., & Buja, G. (2022) Design and analysis of genetic algorithm and BP neural network based PID control for boost converter applied in renewable power genera-tions. IET Renewable Power Generation, 16(7), 1336-1344. https://doi.org/10.1049/rpg2.12320

Wen, Y., Leng, J., Yu, F., & Yu, C. (2020) Integrated design for underground space environment control of subway stations with atriums using piston ventilation. Indoor and Built Environment, 29(9), 1300-1315. https://doi.org/10.1177/1420326X20941

Xu, S., Wang, H., Tian, X., Wang, T., & Tanikawa, H. (2021) From efficiency to equity: Changing patterns of China's regional transportation systems from an in-use steel stocks perspective. Journal of Industrial Ecology, 26(2), 548-561. https://doi.org/10.1111/jiec.13203

Yang, H., Xu, T., Chen, D., Yang, H., & Pu, L. (2020) Direct modeling of subway ridership at the station level: a study based on mixed geographically weighted regression. Canadian Journal of Civ-il Engineering, 47(5), 534-545. https://doi.org/10.1139/cjce-2018-0727

Yuan, Z., Yan, L., Gao, Y., Zhang, T., & Gao, S. (2021) Virtual parameter learning-based adaptive control for protective automatic train operation. IEEE Transactions on Intelligent Transportation Systems, 22(12), 7943-7954. https://doi.org/10.1109/TITS.2021.3066447

Yin, Y & Zhang, Y. (2021) Environmental pollution evaluation of urban rail transit construction based on entropy weight method. Nature Environment and Pollution Technology, 20(2): 819-824. http://dx.doi.org/10.46488/nept.2021.v20i02.044.

Zhan, S., Sunindijo, R., Loosemore, M., Wang, S., Gu, Y., & Li, H. (2021) Identifying critical factors influencing the safety of Chinese subway construction projects. Engineering Construction and Architectural Management, 28(7), 1863-1886. https://doi.org/10.1108/ECAM-07-2020-0525

Zhang, L., Leong, H., Zhou, M., & Li, Z. (2022) An intelligent train operation method based on event-driven deep reinforcement learning. IEEE Transactions on Industrial Informatics, 18(10), 6973-6980. https://doi.org/10.1109/TII.2021.3138098

Zhu, C., Du, G., Jiang, X., Huang, W., Zhang, D., & Fan, M. (2021) Dual-objective optimization of maximum rail potential and total energy consumption in multi-train subway systems. IEEE Trans-actions on Transportation Electrification, 7(4), 3149-3162. https://doi.org/10.1109/TTE.2021.3062706

Zhang, C., Liu, Y., Zhang, B., Yang, O., Yuan, W., He, L & Wang, Z, L. (2021) Harvesting wind energy by a triboelectric nanogenerator for an intelligent high-speed train system. ACS Energy Let-ters, 6(4): 1490-1499. http://dx.doi.org/10.1021/acsenergylett.1c00368.

Zhao, W., Xu, J., Fei, W., Liu, Z., He, W & Li, G. (2023) The reuse of electronic components from waste printed circuit boards: a critical review. Environmental Science: Advances, 2(2): 196-214. http://dx.doi.org/10.1039/d2va00266c.

Downloads

Published

2024-06-30

Issue

Section

Original articles

How to Cite

Wu, S., Chen, Y. ., & Lin, X. . (2024). Simulation and analysis of optimal energy-saving mode in microfield of underground rail transit. Archives of Transport, 70(2), 27-42. https://doi.org/10.61089/aot2024.hc8s5k97

Share

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

61-70 of 357

You may also start an advanced similarity search for this article.

No Related Submission Found