摘要
随着轨道交通网络规模的扩大和列车运营间隔的缩短,列车牵引能耗在快速增加。因此,通过优化列车的驾驶策略降低牵引能耗,对于轨道交通系统的节能减排具有重大意义。针对列车的驾驶策略优化问题,提出一种基于深度Q网络(DQN)的列车节能驾驶控制方法。首先介绍了传统的列车节能驾驶问题并构造其反问题,即通过分配最少的能耗达到规定运行时分。进一步将该问题转化为有限马尔可夫决策过程(MDP),通过设计状态动作值函数、定义动作策略选取方法等,构建基于DQN方法的列车节能驾驶控制方法。通过实际驾驶数据对DQN进行训练,得到最优的状态动作值函数,并通过该值函数确定最优的能耗分配方案,从而得到最优驾驶策略。最后,以北京地铁亦庄线的实际运营数据设计了仿真算例,对方法的有效性进行验证,并对方法参数进行了敏感度分析。提出的方法可充分利用列车的驾驶数据提升驾驶策略,降低列车牵引能耗,对未来我国智慧城轨的发展具有一定的借鉴意义。
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.
作者
宿帅
朱擎阳
魏庆来
唐涛
阴佳腾
SU Shuai;ZHU Qingyang;WEI Qinglai;TANG Tao;YIN Jiateng(State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China;The State Key Laboratory for Management and Control of Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China)
出处
《智能科学与技术学报》
2020年第4期372-384,共13页
Chinese Journal of Intelligent Science and Technology
基金
国家自然科学基金资助项目(No.61803021,No.U1734210)
北京市自然科学基金资助项目(No.L191015)
关键词
列车节能驾驶
驾驶策略
深度Q网络
energy-efficient train driving
driving strategy
deep Q-network
