Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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.
Czasopismo
Rocznik
Tom
Strony
27--42
Opis fizyczny
Bibliogr. 32 poz., rys., tab., wykr.
Twórcy
autor
- Yangzhou Polytechnic Institute, Department of Transportation Engineering, Yangzhou, China
autor
- School of Transportation, Jilin Railway Technology College, Jilin, China
autor
- Yangzhou Polytechnic Institute, Department of Transportation Engineering, Yangzhou, China
Bibliografia
- [1] 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 Organizations, 22(3), 246-271. https://doi.org/10.1504/IJNVO.2020.10027652.
- [2] 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.
- [3] 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.
- [4] Cai, Y., & Chen, J. (2021) Independent cover is geometric 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.
- [5] 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.
- [6] Hou, B., Zeng, Q., Fei, L., & Li, J. (2020) Noise evaluation method for urban rail transit underground station platforms. Journal of Tsinghua University (Science and Technology), 61(1), 57-63. https://doi.org/10.16511/j.cnki.qhdxxb.2020.22.019.
- [7] 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.
- [8] 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.
- [9] Kong, L., Wu, Z., Chen, G., Qiu, M., Mumtaz, S., & Podriguse, J. (2020) Crowd sensing based cross-operator switch in rail transit systems. IEEE Transactions on Communications, 68(12), 7938-7947. https://doi.org/10.1109/TCOMM.2020.3019527.
- [10] 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.
- [11] 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 universal model approach. Journal of Financial Management of Property and Construction, 25(2), 247-271. https://doi.org/10.1108/JFMPC-08-2019-0068.
- [12] Muniandi, G. (2020) Block chain-enabled virtual coupling of automatic train operation fitted mainline trains for railway traffic conflict control. IET Intelligent Transport Systems, 14(4), 611-619. https://doi.org/10.1049/iet-its.2019.0694.
- [13] Meng, X., Gu, A., Wu, X., Zhou, L., Zhou, J., Liu, B & Mao, Z. (2021) Status quo of China hydrogen 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.
- [14] 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. Journal of the Indian Institute of Science, 2022, 102(1): 11-37. http://dx.doi.org/10.1007/s41745-021-00275-9.
- [15] 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.
- [16] Novak H, Lešić V, Vašak M. Energy-efficient model predictive train traction control with incorporated traction system efficiency. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(6): 5044-5055. https://doi.org/10.1109/TITS.2020.3046416.
- [17] 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.
- [18] 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.
- [19] 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.
- [20] 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.
- [21] 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.
- [22] 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 generations. IET Renewable Power Generation, 16(7), 1336-1344. https://doi.org/10.1049/rpg2.12320.
- [23] 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.
- [24] 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.
- [25] 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 Civil Engineer-ing, 47(5), 534-545. https://doi.org/10.1139/cjce-2018-0727.
- [26] 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.
- [27] 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.
- [28] 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.
- [29] 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.
- [30] 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 Transactions on Transportation Electrification, 7(4), 3149-3162. https://doi.org/10.1109/TTE.2021.3062706.
- [31] 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 Letters, 6(4): 1490-1499. http://dx.doi.org/10.1021/acsenergylett.1c00368.
- [32] 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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7d311eb9-973b-4f2d-88d6-1ce71aa2a282
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