This paper presents an online train control convex model based on the shrinking horizon model predictive control (SHMPC) framework, which integrates train-network coupling characteristics to enable real-time prediction and updating of the train’s future states and traction power supply system (TPSS) network topology. The study aims to reduce traction energy consumption at the TPSS level. The results indicate that the minimum single iteration optimization time of the proposed model is 0.109 s, making it suitable for application in automatic train operation (ATO) systems. Furthermore, while the proposed model achieves the minimum TPSS traction energy consumption, this optimal solution does not equate to minimizing the train’s mechanical energy consumption. When targeting minimum TPSS traction energy consumption, the proposed iterative optimization model can achieve a traction energy saving rate of 8.95% and 8.23%.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Energy-efficient Train Control: Online Train Control Considering Train-TPSS Integration


    Beteiligte:
    Peng, Yang (Autor:in) / Luo, Haifeng (Autor:in) / Lu, Shaofeng (Autor:in)


    Erscheinungsdatum :

    11.04.2025


    Format / Umfang :

    4196788 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Energy-efficient train control

    Howlett, Philip G. / Pudney, Peter J. | TIBKAT | 1995


    Energy-Efficient Train Control by Multi-Train Dynamic Cooperation

    Sun, Xubin / Lu, Hong / Dong, Hairong | IEEE | 2017



    Train control circuit, train and train control method

    WANG TIANNONG / ZHANG KUN / CHEN BO et al. | Europäisches Patentamt | 2021

    Freier Zugriff

    Train control method, train control device and train

    WANG XIN / SUN JINGHUI / HU ZHENGWEI et al. | Europäisches Patentamt | 2020

    Freier Zugriff