Speed Optimal-setting of Electric Multiple Units Based on Energy-efficient Operation

YANG Hui; ZHANG Qiongjie; ZHANG Kunpeng; LI Zhongqi; FU Yating

doi:10.3724/SP.J.1219.2014.00334

Volume 43 Issue 3

Jun. 2014

Turn off MathJax

Article Contents

Abstract

References

INFORMATION AND CONTROL > 2014 > 43(3): 334-338. > DOI: 10.3724/SP.J.1219.2014.00334 CSTR: 32166.14.xk.2014.00334

YANG Hui, ZHANG Qiongjie, ZHANG Kunpeng, LI Zhongqi, FU Yating. Speed Optimal-setting of Electric Multiple Units Based on Energy-efficient Operation[J]. INFORMATION AND CONTROL, 2014, 43(3): 334-338. DOI: 10.3724/SP.J.1219.2014.00334

Citation:

PDF (1503 KB)

Speed Optimal-setting of Electric Multiple Units Based on Energy-efficient Operation

1.
School of Electric and Electronic Engineering, East China Jiaotong University, Nanchang 330013, China
2.
Key Laboratory of Advanced Control & Optimization of Jiangxi Province, Nanchang 330013, China

More Information

Received Date: September 21, 2013
Revised Date: March 25, 2014
Published Date: June 19, 2014

Graphical Abstract

Abstract

Abstract

The operation process of electric multiple units (EMUs) is characterized by the complex running environment and the frequent changes of running conditions. Based on the traction energy consumption and running time mechanism models,the data-driven RBF (radial basis function) neural network is exploited to build models of energy consumption and running time with coasting points. GA (genetic algorithm) is then used to optimize the RBF neural network model; it obtains the speed optimal-setting curve satisfying the energy-efficient running conditions. Finally,simulation results on CRH380AL running data show the effectiveness of the proposed method.
- electric multiple units,
- energy consumption,
- coasting point,
- RBF neural network,
- genetic algorithm optimization

FullText(HTML)

References (20)

References

[1]	Hairong D,Bin N,Baigen C,et al. Automatic train control system development and simulation for high-speed railways[J]. IEEE Circuits and Systems Magazine,2010,10(2):6-18.
[2]	Li L,Dong W. A minimal-energy driving strategy for high-speed electric train [J]. Control Theory Application,2012,10(3):280-286.
[3]	张济民,吴汶麒,张树京. 列车节能运行模型和操纵策略优化[J]. 上海交通大学学报,2000,34(12):35-41. Zhang J M,Wu W Q,Zhang S J. Energy-efficient models and optimal strategies for train operation[J]. Journal of Shanghai Jiaotong University,2000,34(12):35-41.
[4]	崔恒斌,冯晓云,王青元,等. 制动利用率对高速列车节能操纵策略的影响[J]. 铁道学报,2012,34(8):13-19. Cui H B,Feng X Y,Wang Q Y,et al. Influence of breaking efficiency on high-speed train energy-saving driving strategies[J]. Journal of the China Railway Society,2012,34( 8):13-19.
[5]	丁勇,毛保华,刘海东. 列车节能运行模拟系统的研究[J]. 北方交通大学学报,2004,28(2):76-81. Ding Y,Mao B H,Liu H D. Study on train movement simulation for saving energy[J]. Journal of Northern Jiaotong Univerity,2004,28(2):76-81.
[6]	路飞,宋沐民,李晓磊,等. 基于事件的控制技术在地铁列车运行中的应用[J]. 中国铁道科学,2006,27(4):106-111. Lu F,Song M M,Li X L,et al. Event-based control technology and the application in subway train operation[J]. China Railway Science,2006,27(4):106-111.
[7]	Liu R. F,Golovitcher I M. Engergy-efficient operation of rail vehicles[J]. Transportation Research:Part A,2003,37(10):917-932.
[8]	卢启衡,冯晓云,王青元. 基于遗传算法的追踪列车节能优化[J]. 西南交通大学学报,2012,47(2):265-270. Lu Q H,Feng X Y,Wang Q Y. Energy-saving optimal control of following trains based on genetic algorithm[J]. Journal of Southwest Jiaotong University,2012,47(2):265-270.
[9]	朱金陵,李会超,王青元. 列车节能控制的优化分析[J]. 中国铁道科学,2008,29(2):104-108. Zhu J L,Li H C,Wang Q Y. Optimization analysis on the energy saving control for trains [J]. China Railway Science,2008,29(2):104-108.
[10]	Feng X S. Optimization of target speeds of high-speed railway trains for traction energy saving and transport efficiency improvement[J]. Energy Policy,2011,39(12):7658-7665.
[11]	Acikbas S,Soylemez M T. Coasting point optimisation for mass rail transit lines using artificial neural networks and genetic algorithms[J]. IET Electric Power Applications,2008,2(3):172-182.
[12]	张昭昭,乔俊飞. 基于在线减法聚类的RBF神经网络结构设计[J]. 控制与决策,2012,27(7):997-1002. Zhang Z Z,Qiao J F. Design RBF neural network architecture based on online subtractive clustering[J]. Control and Decision,2012,27(7):997-1002.
[13]	席剑辉,王海丹. 基于RBF改进网络的多变量序列建模和预测[J]. 信息与控制,2012,41(2):160-164. Xi J H,Wang H D. Modeling and prediction of multivariate series based on an improved RBF network[J]. Information and Control,2012,41(2):160-164.
[14]	Zhang R C,Zhao H C,Yu J Y. A three-dimensional self-adaptive region fuzzy guidance law based on RBF neural networks[J]. International Journal of Modelling,Identification and Control,8(3):184-190.
[15]	Li M,Yang X Q. Genetic algorithm with dynamic regional multispecies[J]. Acta Automatica Sinica,2003,29(2):212-218.
[16]	周琨,夏洪山. 基于启发式遗传算法的航班勤务编排算法[J]. 信息与控制,2010,39(3):361-366. Zhou K,Xia H S. Heuristic genetic algorithm based airlines duty planning[J]. Information and Control,2010,39(3):361-366.
[17]	Wong K K,Ho T K. Dynamic coast control of train movement with genetic [J]. International Journal of Systems Science,2004,35(13/14):835-846.
[18]	石红国,彭其渊,郭寒英. MRT列车运行模拟模型的多目标改进遗传算法[J]. 西南交通大学学报,2006,41(5):658-662. Shi H G,Peng Q Y Guo H Y. Improved multi-objective GA for MRT train operation simulation model[J]. Journal of South-west Jiaotong University,2006,41(5):658-662.
[19]	Chung J W,Oh S M,Choi I C. A hybrid genetic algorithm for train sequencing in the Korean railway[J]. The International Journal of Management Science,2009,37(3):555-565.
[20]	Jiang J. Traction system parameter matching design and research of new-generation high-speed EMUs[J]. Electric Drive for Locomotives,2011(3):9-12.

[1]	YAN Ming, GAO Zhe. Congestion Control Algorithm for the Internet Based on Adaptive Neural Network Terminal Sliding Mode[J]. INFORMATION AND CONTROL, 2014, 43(5): 558-563. DOI: 10.13976/j.cnki.xk.2014.0558
[2]	HAN Min, JIANG Liwen, ZHAO Yao. Endpoint Prediction Model of Basic Oxygen Furnace Steelmaking Based on PSO-ICA and RBF Neural Network[J]. INFORMATION AND CONTROL, 2010, 39(1): 82-87.
[3]	HAN li, Shi Li-ping, XU Zhi-gao. A Pruning Algorithm for RBF Neural Network Based on Rough Sets[J]. INFORMATION AND CONTROL, 2007, 36(5): 604-609,615.
[4]	LI Dong-mei, WANG Zheng-ou. An Algorithm for Computing Lyapunov Exponents of a Dynamical System Based on RBF Neural Networks[J]. INFORMATION AND CONTROL, 2004, 33(5): 523-526.
[5]	LI Dong-mei, WANG Zheng-ou. An Algorithm for Computing Lyapunov Exponents of a Dynamical System Based on RBF Neural Networks[J]. INFORMATION AND CONTROL, 2004, 33(5): 523-526.
[6]	WANG Hua-qiu, CAO Chang-xiu, ZHANG Bang-li. Application of Incremental Genetic RBF NN to Hot Metal Desulfurization Pretreatment[J]. INFORMATION AND CONTROL, 2004, 33(1): 89-92.
[7]	WANG Hua-qiu, CAO Chang-xiu, ZHANG Bang-li. Application of Incremental Genetic RBF NN to Hot Metal Desulfurization Pretreatment[J]. INFORMATION AND CONTROL, 2004, 33(1): 89-92.
[8]	WANG Xue-lei, SHAO Hui-he, LI Ya-fen. AN ONLINE TRAINING ALGORITHM FOR RBF NEURAL NETWORK AND ITS APPLICATION IN NONLINEAR CONTROL[J]. INFORMATION AND CONTROL, 2001, 30(3): 249-253.
[9]	XIE Guang-jun, ZHUANG Zhen-quan. A PREDICTIVE ALGORITHM BASED ON DIFFERENCE RADIAL BASIS FUNCTION NEURAL NETWORKS MODEL FOR TIME SERIES AND ITS APPLICATION[J]. INFORMATION AND CONTROL, 2000, 29(5): 421-424.
[10]	WANG Xudong, SHAO Huihe. THE THEORY OF RBF NEURAL NETWORK AND ITS APPLICATION IN CONTROL[J]. INFORMATION AND CONTROL, 1997, 26(4): 272-284.

Cited By

Get Citation

PDF

XML

Article views (1179) PDF downloads (470)

Speed Optimal-setting of Electric Multiple Units Based on Energy-efficient Operation

Abstract

References

Related Articles

Catalog

Related

Speed Optimal-setting of Electric Multiple Units Based on Energy-efficient Operation

Abstract

References

Related Articles

Catalog

Related

Export File

Citation

Format

Content