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Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology

Author

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  • Youneng Huang

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China
    National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

  • Chen Yang

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

  • Shaofeng Gong

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

Abstract

More and more lines are using the Communication Based Train Control (CBTC) systems in urban rail transit. Trains are operated by tracking a pre-determined target speed curve in the CBTC system, so one of the most effective ways of reducing energy consumption is to fully understand the optimum curves that should prevail under varying operating conditions. Additionally, target speed curves need to be calculated with optimum real-time performance in order to cope with changed interstation planning running time. Therefore, this paper proposes a fast and effective algorithm for optimization, based on a two-stage method to find the optimal curve using a max-min ant colony optimization system, using approximate calculations of a discrete combination optimization model. The first stage unequally discretizes the line based on static gradient and speed limit in low-density and it could conduct a comprehensive search for viable energy saving target speed curves. The second stage unequally discretizes the line based on first stage discretion results, it makes full use of first-stage optimization information as pheromone, quickly optimizing the results to satisfy real-time demands. The algorithm is improved through consideration of the experience of train drivers. Finally, the paper presents some examples based on the operation data of Beijing Changping Subway Line, which is using CBTC system. The simulation results show that the proposed approach presents good energy-efficient and real-time performance.

Suggested Citation

  • Youneng Huang & Chen Yang & Shaofeng Gong, 2016. "Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology," Energies, MDPI, vol. 9(8), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:626-:d:75606
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    References listed on IDEAS

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    1. Liu, Rongfang (Rachel) & Golovitcher, Iakov M., 2003. "Energy-efficient operation of rail vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 917-932, December.
    2. Youneng Huang & Xiao Ma & Shuai Su & Tao Tang, 2015. "Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm," Energies, MDPI, vol. 8(12), pages 1-19, December.
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    Cited by:

    1. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    2. 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.
    3. Aredah, Ahmed & Fadhloun, Karim & Rakha, Hesham A., 2024. "Energy optimization in freight train operations: Algorithmic development and testing," Applied Energy, Elsevier, vol. 364(C).
    4. Zhou, Wenliang & Huang, Yu & Deng, Lianbo & Qin, Jin, 2023. "Collaborative optimization of energy-efficient train schedule and train circulation plan for urban rail," Energy, Elsevier, vol. 263(PA).

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