IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i22p6093-d448605.html
   My bibliography  Save this article

Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros

Author

Listed:
  • Xiaowen Wang

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Zhuang Xiao

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Mo Chen

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Pengfei Sun

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Qingyuan Wang

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

  • Xiaoyun Feng

    (School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China)

Abstract

Nowadays, most metro vehicles are equipped with an automatic train operation (ATO) system, and the speed control method, combining cruise speed planning and proportional-integral-derivative (PID) control, is widely used. The automation is achieved, and the energy-efficient can be improved. This paper presents an improved artificial bee colony algorithm for speed profile optimization with coast mode and an adaptive terminal sliding mode method for speed tracking. Specifically, a multi-objective optimization model is established, which considers energy consumption, comfortableness, and punctuality. Then, a novel artificial bee colony algorithm named regional reinforcement artificial bee colony (RR-ABC) is designed, to search the optimal speed profile with coast mode, in which some improvements are made to speed up convergence and to avoid local optimal solutions. For speed-tracking control, the adaptive terminal sliding mode controller (ATSMC) is used to improve the speed error, robustness, and energy saving. In addition, a disturbance observer (DOB) is designed to improve the anti-interference ability of the system and further improve the robustness and anti-disturbance, which are also conducive to speed error and energy saving. Finally, the line and train data of the Qingdao Metro Line 6 are used for simulation, which proves the effectiveness of the study. Specific to the energy saving rate, and compared with normal algorithms, RR-ABC with coast mode is approximately 9.55%, and ATSMC+DOB is 7.58%.

Suggested Citation

  • Xiaowen Wang & Zhuang Xiao & Mo Chen & Pengfei Sun & Qingyuan Wang & Xiaoyun Feng, 2020. "Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros," Energies, MDPI, vol. 13(22), pages 1-29, November.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6093-:d:448605
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/22/6093/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/13/22/6093/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mo Chen & Zhuang Xiao & Pengfei Sun & Qingyuan Wang & Bo Jin & Xiaoyun Feng, 2019. "Energy-Efficient Driving Strategies for Multi-Train by Optimization and Update Speed Profiles Considering Transmission Losses of Regenerative Energy," Energies, MDPI, vol. 12(18), pages 1-25, September.
    2. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2016. "A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 424-445.
    3. Fei Shang & Jingyuan Zhan & Yangzhou Chen, 2020. "An Online Energy-Saving Driving Strategy for Metro Train Operation Based on the Model Predictive Control of Switched-Mode Dynamical Systems," Energies, MDPI, vol. 13(18), pages 1-14, September.
    4. Phil Howlett, 2000. "The Optimal Control of a Train," Annals of Operations Research, Springer, vol. 98(1), pages 65-87, December.
    5. Zhaoxiang Tan & Shaofeng Lu & Kai Bao & Shaoning Zhang & Chaoxian Wu & Jie Yang & Fei Xue, 2018. "Adaptive Partial Train Speed Trajectory Optimization," Energies, MDPI, vol. 11(12), pages 1-33, November.
    6. Li, Xiang & Lo, Hong K., 2014. "An energy-efficient scheduling and speed control approach for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 73-89.
    7. Agostinho Rocha & Armando Araújo & Adriano Carvalho & João Sepulveda, 2018. "A New Approach for Real Time Train Energy Efficiency Optimization," Energies, MDPI, vol. 11(10), pages 1-21, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maryna Bulakh & Leszek Klich & Oleksandra Baranovska & Anastasiia Baida & Sergiy Myamlin, 2023. "Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car," Energies, MDPI, vol. 16(18), pages 1-12, September.
    2. Wang, Xuekai & Tang, Tao & Su, Shuai & Yin, Jiateng & Gao, Ziyou & Lv, Nan, 2021. "An integrated energy-efficient train operation approach based on the space-time-speed network methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    3. Hongliang Pan & Hao Wang & Chenglong Yu & Junjie Zhao, 2022. "Displacement-Constrained Neural Network Control of Maglev Trains Based on a Multi-Mass-Point Model," Energies, MDPI, vol. 15(9), pages 1-15, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Canca, David & Zarzo, Alejandro, 2017. "Design of energy-Efficient timetables in two-way railway rapid transit lines," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 142-161.
    2. Ye, Hongbo & Liu, Ronghui, 2016. "A multiphase optimal control method for multi-train control and scheduling on railway lines," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 377-393.
    3. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2017. "Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 22-37.
    4. Zhaoxiang Tan & Shaofeng Lu & Kai Bao & Shaoning Zhang & Chaoxian Wu & Jie Yang & Fei Xue, 2018. "Adaptive Partial Train Speed Trajectory Optimization," Energies, MDPI, vol. 11(12), pages 1-33, November.
    5. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2016. "The key principles of optimal train control—Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 482-508.
    6. Cheng Gong & Shiwen Zhang & Feng Zhang & Jianguo Jiang & Xinheng Wang, 2014. "An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One," Energies, MDPI, vol. 7(11), pages 1-25, November.
    7. Luan, Xiaojie & Wang, Yihui & De Schutter, Bart & Meng, Lingyun & Lodewijks, Gabriel & Corman, Francesco, 2018. "Integration of real-time traffic management and train control for rail networks - Part 2: Extensions towards energy-efficient train operations," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 72-94.
    8. Jianqiang Liu & Nan Zhao, 2017. "Research on Energy-Saving Operation Strategy for Multiple Trains on the Urban Subway Line," Energies, MDPI, vol. 10(12), pages 1-19, December.
    9. Wang, Xuekai & Tang, Tao & Su, Shuai & Yin, Jiateng & Gao, Ziyou & Lv, Nan, 2021. "An integrated energy-efficient train operation approach based on the space-time-speed network methodology," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 150(C).
    10. Gupta, Shuvomoy Das & Tobin, J. Kevin & Pavel, Lacra, 2016. "A two-step linear programming model for energy-efficient timetables in metro railway networks," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 57-74.
    11. Wang, Chao & Meng, Xin & Guo, Mingxue & Li, Hao & Hou, Zhiqiang, 2022. "An integrated energy-efficient and transfer-accessible model for the last train timetabling problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    12. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2018. "The two-train separation problem on non-level track—driving strategies that minimize total required tractive energy subject to prescribed section clearance times," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 135-167.
    13. Jie Yang & Limin Jia & Shaofeng Lu & Yunxiao Fu & Ji Ge, 2016. "Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution," Energies, MDPI, vol. 9(10), pages 1-27, September.
    14. Scheepmaker, Gerben M. & Goverde, Rob M.P. & Kroon, Leo G., 2017. "Review of energy-efficient train control and timetabling," European Journal of Operational Research, Elsevier, vol. 257(2), pages 355-376.
    15. Wang, Pengling & Goverde, Rob M.P., 2019. "Multi-train trajectory optimization for energy-efficient timetabling," European Journal of Operational Research, Elsevier, vol. 272(2), pages 621-635.
    16. Li, Xiang & Lo, Hong K., 2014. "Energy minimization in dynamic train scheduling and control for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 269-284.
    17. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2016. "A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 424-445.
    18. Zhou, Li & Yang, Xin & Wang, Huan & Wu, Jianjun & Chen, Lei & Yin, Haodong & Qu, Yunchao, 2020. "A robust train timetable optimization approach for reducing the number of waiting passengers in metro systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    19. Aredah, Ahmed & Fadhloun, Karim & Rakha, Hesham A., 2024. "Energy optimization in freight train operations: Algorithmic development and testing," Applied Energy, Elsevier, vol. 364(C).
    20. Haahr, Jørgen Thorlund & Pisinger, David & Sabbaghian, Mohammad, 2017. "A dynamic programming approach for optimizing train speed profiles with speed restrictions and passage points," Transportation Research Part B: Methodological, Elsevier, vol. 99(C), pages 167-182.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:22:p:6093-:d:448605. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.