The Shenmu-Shuozhou Railway of Shenhua Baoshen Railway Group Co., Ltd. operates 10,000t heavy-haul trains using centralized traction on intricate railway conditions. The application of the automatic train operation (ATO) mode has accentuated train impulse issues arising from frequent traction adjustments during train operation, underscoring the need for enhanced stability of ATO. This paper presents an optimization control algorithm based on a dynamic evaluation model of train states, tailored to the specific conditions and operational requirements of the Shenmu-Shuozhou Railway. This algorithm features a multi-particle model and a train state evaluation model for heavy-haul trains, evaluating the development trend of train states while incorporating constraints related to the loading/unloading slope in levels. The proposed algorithm was applied in an experiment on the Shenmu-Shuozhou Railway. During the departing stage, the optimization algorithm effectively improved operational stability on steep grades, and increased the departing speed by about 11.1%. During the cruising stage, train impulses was effectively suppressed pre and post neutral-zone crossings, and the train running speed increased by about 0.9%. Moreover, the trend of simulated coupling force changes aligned closely with actual traction loading, and the maximum locomotive-wagon coupling force was reduced by about 26.3%. During the parking stage, the maximum amplitude of train acceleration decreased by 71.4%, and the average impulse count decreased by 60%. The experimental results demonstrate the effectiveness of the proposed algorithm in improving the ATO stability for 10,000t heavy-haul trains operating under centralized traction.


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    Titel :

    Research on Stability Optimization for Automatic Train Operation of Heavy-haul Trains of Baoshen Railway


    Beteiligte:
    FU Shangyuan (Autor:in)


    Erscheinungsdatum :

    2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




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