Volume 39 Issue 4
Aug.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(4): 84-91
MA Yangyang, MENG Xuelei, JIA Baotong, REN Yuanyuan, QIN Yongsheng. An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation[J]. Journal of Transport Information and Safety, 2021, 39(4): 84-91. doi: 10.3963/j.jssn.1674-4861.2021.04.011
Citation: MA Yangyang, MENG Xuelei, JIA Baotong, REN Yuanyuan, QIN Yongsheng. An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation[J]. Journal of Transport Information and Safety, 2021, 39(4): 84-91. doi: 10.3963/j.jssn.1674-4861.2021.04.011
shu

An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation

doi: 10.3963/j.jssn.1674-4861.2021.04.011

  • School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China

  • Received Date: 2021-04-25
    Abstract
  • Reasonable arranging the operation mode of the train in the section can reduce the energy consumption of train operation. A determination strategy of train operating conditions based on the speed limit of the interval is adopted to determine the operating condition of the train. The energy consumption of the train is used as the optimization objective, and the distance, time, and speed limit of the train are used as the constraints. Time deviation penalty during train operation is added to the objective function to develop a mathematical model of energy-saving optimization for high-speed railway train operation, and the improved artificial bee colony algorithm based on Gaussian mutation and chaotic disturbance is used to solve the optimization model. The model and algorithm are verified with CRH3-350 multi-unit data as an example, the solution results show that the energy consumption can be saved by 2.5% when time deviation penalty during train operation is considered. Compared with the basic artificial bee colony algorithm and particle swarm algorithm, the improved artificial bee colony algorithm has improved the target value by 4.2% and 4.1%, respectively. Adopting the determinative strategy based on the interval speed limit combined with the energy-consumption optimization model can meet the required train operation conditions under different speed limits and different intervals. It shows that the established model and the designed algorithm have good problem-solving efficiency and optimized quality.

     

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