Abstract
In recent years, as the development of urban rail transit has effectively slowed down the congestion of urban traffic, more and more cities have focused on promoting the development of urban rail transit. But those trains of urban rail transit have also generated more and more energy consumption. In order to alleviate the energy shortage and promote energy conservation and emission reduction, it is of great significance to reduce the energy consumption of subway trains. The optimization control of energy-efficient driving is to reduce the energy consumption of trains when operating and improve the operation efficiency. The traditional particle swarm optimization algorithm is easy to fall into the local optimum and the global performance is not so good. In this paper, the genetic particle swarm optimization algorithm (GA-PSO) is used, combining the advantages of genetic algorithm and particle swarm algorithm, to optimize the energy consumption of trains. And MATLAB is used to carry out modeling and simulation analysis. Compared with the traditional particle swarm and genetic algorithm, the optimized algorithm is verified that it can effectively reduce the energy consumption of trains. The results show that the energy cost is reduced by 18.8%, which verifies the effectiveness of the developed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Howlett P (1990) An optimal strategy for the control of a train. J Aust Math Soc Ser B Appl Math 31(4):454–471
Cheng J, Howlett P (1992) Application of critical velocities to the minimisation of fuel consumption in the control of trains. Automatica 28(1):165–169
Khmelnitsky E (2000) On an optimal control problem of train operation. IEEE Trans Autom Control 45(7):1257–1266
Liu R, Golovitcher IM (2003) Energy-efficient operation of rail vehicles. Transp Res Part A 37(10):917–932
Chang CS, Sim SS (1997) Optimising train movements through coast control using genetic algorithms. IEEE Proc Electr Power Appl 144(1):65–73
Han SH, Byen YS, Baek JH et al (1999) An optimal automatic train operation (ATO) control using genetic algorithms (GA). In: Proceedings of the IEEE region 10 conference. TENCON 99.
Acikbas S, Soylemez MT (2008) Coasting point optimization for mass rail transit lines using artificial neural networks and genetic algorithms. IET Electr Power Appl 2(3):172–182
Wang Z, Hou Z (2014) Research on energy-saving optimization control of urban rail transit train based on MGASA. Appl Res Comput 31(12):3575–3579
Fan L (2016) Optimization of train recommendation speed curve based on ant colony algorithm. Beijing Jiaotong University, Beijing
Huang Y, Shaofeng G, Yuan C et al (2016) Energy-saving driving optimization model of urban rail train based on particle swarm optimization algorithm. J Traffic Transp Eng 16(2):118–124
Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In: MHS’95. Proceedings of the sixth international symposium micro machine and human science, Institute of electric and electronic engineer
Acknowledgements
This work was financially supported by the National Natural Science Foundation of China (Grant No. 61673049).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhao, L., Peng, J., Wang, J., Zhou, Y. (2020). Optimization Control of Energy-Efficient Driving for Trains in Urban Rail Transit Based on GA-PSO Algorithm. In: Jia, L., Qin, Y., Liu, B., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 638. Springer, Singapore. https://doi.org/10.1007/978-981-15-2862-0_75
Download citation
DOI: https://doi.org/10.1007/978-981-15-2862-0_75
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2861-3
Online ISBN: 978-981-15-2862-0
eBook Packages: EngineeringEngineering (R0)