Maglev trains have line adaptability, low noise, low vibration, and have the potential for ultra-high operating speeds. Although maglev trains eliminate the energy consumption caused by wheel-rail friction, the significant aerodynamic energy consumption resulting from high-speed operation cannot be ignored. However, current research on energy consumption optimization for high-speed maglev trains remains relatively limited. This paper establishes an operation optimization mathematical model for high-speed maglev trains and proposes an energy-saving optimization method based on genetic algorithm. The speed curve is optimized by adjusting the time points for the transition of the train’s working conditions in the traction, uniform speed, idling, and deceleration phases, and the study provides an in-depth analysis of the train’s energy consumption and running time under different operating conditions. The results indicate that by setting different weighting coefficients, the proposed method can flexibly adjust the balance between travel time and energy consumption. In addition, the selection of maximum speed has a significant impact on both energy consumption and travel time. When the maximum speed is reduced to 300 km/h, the energy consumption optimization rate reaches 28.6%, with an increase in travel time of 11.24%. The optimization method proposed in this paper can provide theoretical support and technical guidance for the energy-saving operation of high-speed maglev trains.


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    Title :

    Optimization of Energy-Saving Operation of High-Speed Maglev Traction Based on Genetic Algorithm


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Yao, Dechen (editor) / Ma, Feng (editor) / Zhang, Liguo (editor) / Chen, Yuejian (editor) / Xue, Qingwan (editor) / Gao, Wanying (author) / Huang, Jingyu (author) / Zhang, Ziyang (author) / Fu, Xinxin (author)

    Conference:

    International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024



    Publication date :

    2025-03-28


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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