A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were modeled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Energy-Efficient Train Operation Using Nature-Inspired Algorithms


    Beteiligte:


    Erscheinungsdatum :

    2017




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Intelligent Adaptive Equalizer Design Using Nature Inspired Algorithms

    Ghosh, Shriya / Banerjee, Subhabrata | IEEE | 2018


    Energy-Efficient Automatic Train Operation Method

    LEE JAE WON | Europäisches Patentamt | 2023

    Freier Zugriff

    Introduction to Energy-Efficient Train Operation

    Tian, Zhongbei / Liu, Xiao / Su, Shuai et al. | Springer Verlag | 2023



    Efficient Real-Time Train Operation Algorithms With Uncertain Passenger Demands

    Yin, Jiateng / Chen, Dewang / Yang, Lixing et al. | IEEE | 2016