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Energy-Efficient Operation Curve Optimization for High-Speed Train Based on GSO Algorithm

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Recent Advances in Sustainable Energy and Intelligent Systems (LSMS 2021, ICSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1468))

Abstract

The demand for high-speed automatic train operation (ATO) system brings new opportunities and challenges for the high-speed railway field. The requirements of energy consumption, punctuality, safety and smoothness are also increasing, especially the research on energy saving of high-speed trains ushers in a broader development space. This paper proposes quantitative functions of four energy-saving performance indexes and the multi-objective optimization model of train operation curve considering the characteristics of high-speed train ATO system. An energy-saving optimization method of train operation curve based on Glowworm Swarm Optimization (GSO) algorithm is proposed. The simulation results show that the train operation using the high-speed train operation curve optimization method based on the GSO algorithm can save about 16.9% of electrical energy consumption per kilometer compared with that by other optimization algorithms, which verifies the effectiveness of the method. The result from this paper provides theoretical support for practical application.

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References

  1. Xie, T., Wang, S., Zhao, X., Zhang, Q.: Optimization of train energy-efficient operation using simulated annealing algorithm. In: Li, K., Li, S., Li, D., Niu, Q. (eds.) ICSEE 2012. CCIS, vol. 355, pp. 351–359. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37105-9_39

    Chapter  Google Scholar 

  2. Bai, Y., Ho, T.K., Mao, B.: Energy-efficient locomotive operation for Chinese mainline railways by fuzzy predictive control. IEEE Trans. Intell. Transp. Syst. 15(3), 938–948 (2014)

    Article  Google Scholar 

  3. Zhou, L.S., Tong, L. (Carol), Chen, J.H., et al.: Joint optimization of high-speed train timetables and speed profiles: a unified modeling approach using space-time-speed grid networks. Transp. Res. Part B 97, 157–181 (2017)

    Google Scholar 

  4. Liu, S., Cao, F., Xun, J., Wang, Y.: Energy-efficient operation of single train based on the control strategy of ATO. In: IEEE International Conference on Intelligent Transportation Systems, pp. 2580–2586 (2015)

    Google Scholar 

  5. Miao, Z., Qi, Z., Zi, X.Z.: A study on energy-saving optimization for high-speed railways train based on Q-learning algorithm. Rail Transp. Econ. 41(12), 111–117 (2019). (in Chinese)

    Google Scholar 

  6. Tang, T., Xun, J., Cao, F., Wang, Y.H., Soo, S.: Research on energy-efficient driving strategy in Beijing Yizhuang Line. J. Beijing Jiaotong Univ. 40(4), 19–24 (2016). (in Chinese)

    Google Scholar 

  7. Hao, G., Zhang, Y.D., Jin, G., Zhi, Z.: Optimization of energy-efficient train operation based on dynamic programming approach. J. China Railw. Soc. 42(08), 76–84 (2020). (in Chinese)

    Google Scholar 

  8. Liang, Z.C., Wang, Q.Y., He, K.: Energy saving control of electric multiple unit train based on maximum principle. J. China Railw. Soc. 37(10), 16–25 (2015). (in Chinese)

    Google Scholar 

  9. He, Z.Y.: Research on automatic train operation algorithm based on non-parametric iterative learning control. J. China Railw. Soc. 42(12), 90–96 (2020). (in Chinese)

    Google Scholar 

  10. Li, K.P., Gao, Z.Y.: Optimal method of energy saving train operation for railway network. J. Transp. Syst. Eng. Inf. Technol. 9(04), 90–96 (2009). (in Chinese)

    Google Scholar 

  11. Bhanu, P., Krishnanand, K.N., Debasish, G.: Glowworm swarm-based optimization algorithm for multimodal functions with collective robotics applications. Multiagent Grid Syst. 2(3), 209–222 (2005)

    MATH  Google Scholar 

  12. Keskin, K., Karamancioglu, A.: Application of firefly algorithm to train operation. In: 2016 IEEE In: 8th International Conference on Intelligent Systems (IS), pp. 692–697 (2016)

    Google Scholar 

  13. Huang, Z.X., Zhou, Y.Q.: Using Glowworm swarm optimization algorithm for clustering analysis. J. Convergence Inf. Technol. 6(2), 78–85 (2011)

    Article  Google Scholar 

  14. Kaipa, K.N., Ghose, D.: Glowworm swarm optimization: algorithm development. In: Glowworm Swarm Optimization. SCI, vol. 698, pp. 21–56. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51595-3_2

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Acknowledgment

The authors wish to convey their sincere sense of gratitude to the support of the National Key Research and Development Program of China under Grant 2016YFB1200602-34.

Author information

Authors and Affiliations

  1. School of Traffic and Transportation Engineering, Central South University, Changsha, China

    Wei Li, Sizhe Zhao, Kang Li, Yi Xing, Gaofeng Liu & Jilong Liu

  2. School of Electronic and Electrical Engineering, University of Leeds, Leeds, W Yorkshire, England

    Kang Li

  3. CRSC Research and Design Institute Group Co., Ltd., Beijing, China

    Yi Xing

  4. CRRC QINGDAO SIFANG Co., Ltd., Shandong, China

    Jilong Liu

Authors
  1. Wei Li
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  2. Sizhe Zhao
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  3. Kang Li
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  4. Yi Xing
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  5. Gaofeng Liu
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  6. Jilong Liu
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Editor information

Editors and Affiliations

  1. University of Leeds, Leeds, UK

    Kang Li

  2. University of Cambridge, Cambridge, UK

    Tim Coombs

  3. Beijing Jiaotong University, Beijing, China

    Jinghan He

  4. Queensland University of Technology, Brisbane, QLD, Australia

    Yuchu Tian

  5. Shanghai University, Shanghai, China

    Qun Niu

  6. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Zhile Yang

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Cite this paper

Li, W., Zhao, S., Li, K., Xing, Y., Liu, G., Liu, J. (2021). Energy-Efficient Operation Curve Optimization for High-Speed Train Based on GSO Algorithm. In: Li, K., Coombs, T., He, J., Tian, Y., Niu, Q., Yang, Z. (eds) Recent Advances in Sustainable Energy and Intelligent Systems. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1468. Springer, Singapore. https://doi.org/10.1007/978-981-16-7210-1_12

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  • DOI: https://doi.org/10.1007/978-981-16-7210-1_12

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-7209-5

  • Online ISBN: 978-981-16-7210-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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