Login Register Cart 0
Generic placeholder image

Recent Patents on Mechanical Engineering

Editor-in-Chief

ISSN (Print): 2212-7976
ISSN (Online): 1874-477X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Energy Saving Optimal Operation Strategy of Freight Trains under Complex Scenarios

Author(s): Lingzhi Yi, Cheng Xie*, Yahui Wang, Jiangyong Liu, Chuyang Yi and Wenbo Jiang

Volume 16, Issue 4, 2023

Published on: 04 August, 2023

Page: [237 - 250] Pages: 14

DOI: 10.2174/2212797616666230622143121

Price: $65

Abstract

Background: For the optimization of energy-saving driving of freight trains in complex operating environments, the use of reasonable train maneuvering methods can largely reduce the energy consumption of train traction. Recent patents on energy-efficient maneuvering strategies for complex scenarios of freight trains have been researched.

Objective: Using the receding horizon algorithm and the improved NSGA-II algorithm to solve the target speed curve of freight trains to cope with the complex and changing operating environment, and to explore the recent patents of energy-saving maneuvering strategies for freight trains and methods.

Methods: The recent patents of energy-efficient maneuvering strategies for freight trains in complex scenarios are investigated in this research. A multi-objective optimization model for freight train maneuvering with electrical phasing was developed with the objectives of reducing the traction energy consumption and running time of the train. A method for determining the optimal operating conditions of freight trains under complex line conditions is proposed. The offline optimization of the target speed curve under the electrical phasing constraints of freight trains and the online adjustment under the temporary speed restriction (TSR) are achieved by using the RH-INSGA-II (receding horizon-improved NSGA- II) algorithm.

Results: Combined with an actual freight railroad line data as an example, simulation experiments were conducted and verified with HXD1 electric locomotive hauling 50 C80 wagons.

Conclusion: The speed curve considering the split-phase constraint can effectively reduce the traction energy consumption. The electrical split-phase constraint affects the whole speed optimization process, not only the speed curve at the split-phase zone. Although the traction energy consumption is increased with the addition of the TSR on the line, the RH-INSGA-II algorithm dynamically changes the sequence of optimal train maneuvering conditions according to the planned train running time in order to avoid further amplification of the late train time.

Next »
[1]
Ichikawa K. Application of optimization theory for bounded state variable problems to the operation of train. Bull JSME 1968; 11(47): 857-65.
[http://dx.doi.org/10.1299/jsme1958.11.857]
[2]
Howlett P. An optimal strategy for the control of a train. J Aust Math Soc B Appl Math 1990; 31(4): 454-71.
[http://dx.doi.org/10.1017/S0334270000006780]
[3]
Scheepmaker GM, Goverde RMP. The interplay between energy-efficient train control and scheduled running time supplements. J Rail Trans Plan Manag 2015; 5(4): 225-39.
[http://dx.doi.org/10.1016/j.jrtpm.2015.10.003]
[4]
Cheng LI, Xiaomin WANG. An ATO control strategy based on particle swarm optimization. J Chi Rail Soc 2017; 39(3): 53-8.
[5]
Yanqiang YANG, Haidong LIU, Cunrui MA, Liang XU. Target speed control optimization of train movement for saving energy. J Transp Syst Eng Inf Technol 2019; 19(1): 138-44.
[6]
Dullinger C, Struckl W, Kozek M. Simulation-based multi-objective system optimization of train traction systems. Simul Model Pract Theory 2017; 72: 104-17.
[http://dx.doi.org/10.1016/j.simpat.2016.12.008]
[7]
Huiru ZHANG, Limin JIA, Li WANG. Study on generation of energy saving driving curves of high-speed train based on Pareto multi-objective optimization. J Chi Rail Soc 2021; 43(3): 85-91.
[8]
Cunrui MA, Baohui MAO, Yun BAI, Shenxu DU, Sijia ZHANG. Energy saving operation method for high-speed trains considering passing neutrally phase insulators. J China Railway Sci 2019; 40(4): 137-44.
[9]
Sheng Z. ShangGuan W, Cai BG, Song H. Energy‐optimal study integrated speed trajectories, timetable and the layout of neutral sections for high‐speed railway. IET Intell Transp Syst 2022; 16(8): 1026-41.
[http://dx.doi.org/10.1049/itr2.12193]
[10]
Sicre C, Cucala AP, Fernández-Cardador A. Real time regulation of efficient driving of high speed trains based on a genetic algorithm and a fuzzy model of manual driving. Eng Appl Artif Intell 2014; 29: 79-92.
[http://dx.doi.org/10.1016/j.engappai.2013.07.015]
[11]
Yan XH, Cai BG, Ning B. ShangGuan W. Moving horizon optimization of dynamic trajectory planning for high-speed train operation. IEEE Trans Intell Transp Syst 2016; 17(5): 1258-70.
[http://dx.doi.org/10.1109/TITS.2015.2499254]
[12]
Fernández-Rodríguez A, Fernández-Cardador A, Cucala AP. Real time eco-driving of high speed trains by simulation-based dynamic multi-objective optimization. Simul Model Pract Theory 2018; 84: 50-68.
[http://dx.doi.org/10.1016/j.simpat.2018.01.006]
[13]
Zhong W, Li S, Xu H, Zhang W. On-line train speed profile generation of high-speed railway with energy-saving: A model predictive control method. IEEE Trans Intell Transp Syst 2022; 23(5): 4063-74.
[http://dx.doi.org/10.1109/TITS.2020.3040730]
[14]
He D, Zhang L, Guo S, Chen Y, Shan S, Jian H. Energy-efficient train trajectory optimization based on improved differential evolution algorithm and multi-particle model. J Clean Prod 2021; 304: 127163.
[http://dx.doi.org/10.1016/j.jclepro.2021.127163]
[15]
Minglong XU, Gu LI, Wei LI, Tao ZHANG, Wenlu ZHANG, Fuwei BAI. Analysis of longitudinal force degradation and running safety of heavy-haul combined trains. J Rai Sci Eng 2023; 20(1): 321-32.
[16]
Huazhen YU, Youneng HUANG, Mingzhu WANG, Zhanyuan HE, Xianhong MENG, Yongliang LI. Research on operating strategies of heavy haul train based on genetic algorithm. J Chi RailSoc 2020; 42(7): 110-6.
[17]
Das S, Mullick SS, Suganthan PN. Recent advances in differential evolution – An updated survey. Swarm Evol Comput 2016; 27: 1-30.
[http://dx.doi.org/10.1016/j.swevo.2016.01.004]
[18]
Meng F, Wang L, Xiao Y, Xie G, Zhang D, Zhao F. Multi-objective optimization for the impeller parameters of centrifugal fan based on kriging model and GA-PSO algorithm. Recent Pat Mech Eng 2018; 11(3): 242-50.
[http://dx.doi.org/10.2174/2212797611666180622125804]
[19]
He C, Cheng R, Yazdani D. Adaptive offspring generation for evolutionary large-scale multiobjective optimization. IEEE Trans Syst Man Cybern Syst 2022; 52(2): 786-98.
[http://dx.doi.org/10.1109/TSMC.2020.3003926]
[20]
Albrecht A, Howlett P, Pudney P, Vu X, Zhou P. The key principles of optimal train control—part 2: Existence of an optimal strategy, the local energy minimization principle, uniqueness, computational techniques. Transp Res, Part B: Methodol 2016; 94: 509-38.
[http://dx.doi.org/10.1016/j.trb.2015.07.024]
[21]
Albrecht A, Howlett P, Pudney P, Vu X, Zhou P. The key principles of optimal train control—part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points. Transp Res, Part B: Methodol 2016; 94: 482-508.
[http://dx.doi.org/10.1016/j.trb.2015.07.023]
[22]
Zhang J, Li Z, Wang B. Within-day rolling optimal scheduling problem for active distribution networks by multi-objective evolutionary algorithm based on decomposition integrating with thought of simulated annealing. Energy 2021; 223: 120027.
[http://dx.doi.org/10.1016/j.energy.2021.120027]
[23]
Huilan JIANG, Xing An, Yawei WANG, Jingzhong QIN, Guanchao QIAN. Improved NSGA2 algorithm based on multi-objective planning of power grid with wind farm considering power quality. Proceedings of the CSEE 2015; 35(21): 5405-11.
[24]
Guo Y, Feng W, Zhang G, et al. Fault diagnosis of oil-immersed transformer based on TSNE and IBASA- SVM. Recent Pat Mech Eng 2022; 15(5): 504-14.
[http://dx.doi.org/10.2174/2212797615666220622093515]
[25]
Xiao Y, Cui G. A novel random walk algorithm with compulsive evolution for heat exchanger network synthesis. Appl Therm Eng 2017; 115: 1118-27.
[http://dx.doi.org/10.1016/j.applthermaleng.2017.01.051]
[26]
Zeng GQ, Chen J, Li LM, et al. An improved multi-objective population-based extremal optimization algorithm with polynomial mutation. Inf Sci 2016; 330: 49-73.
[http://dx.doi.org/10.1016/j.ins.2015.10.010]
[27]
Xuan LIN. Energy-efficient driving strategies of electric freight locomotive. In: Southwest Jiaotong University. 2018.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy