The energy consumption in railway system is growing rapidly due to the expanding scale of the railway network and decreased operational headway.Hence, it is of great significant to apply the energy-efficient operation of the vehicles to cut down the energy cost of the railway system.A method for solving the energy-efficient train driving control based on deep Q-network (DQN) approach was proposed.Firstly, the traditional energy-efficient train driving control problem was presented and its inverse problem was formulated, i.e., distributing the least energy consumption units to achieve the scheduled trip time.Moreover, the problem was reformulated as a Markov decision process (MDP) and a DQN-based approach for energy-efficient train driving control was proposed.A DQN was built to approximate the action value function which determines the optimal energy distribution policy and further obtain the optimal driving strategy.Finally, a numerical experiment based on the real-world operational data was proposed to verify the effectiveness of the proposed method and analyze the performance of the proposed method.The driving data of the trains is applied to improve the driving strategy via the proposed method in the paper which reduces the traction energy consumption.It is of significance for the future development of Chinese intelligent urban railway system.


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    Title :

    A DQN-based approach for energy-efficient train driving control


    Contributors:
    Shuai SU (author) / Qingyang ZHU (author) / Qinglai WEI (author) / Tao TANG (author) / Jiateng YIN (author)


    Publication date :

    2020



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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