列车节能优化操纵理论及应用研究

发布时间：2018-07-23 10:36

【摘要】：列车的运行是在一个复杂多变的环境下,由多种因素共同作用的结果。在我国列车的运行控制主要依靠机车驾驶员的经验及操作技术水平。尽管铁路运输的单位能耗逐年降低,但能耗总量依然巨大。因此,研究列车节能优化操纵对于铁路行业的节能减排具有重大意义。本文从列车的区间运行控制和停车制动控制两方面研究了列车节能优化操纵问题,在保证列车运行安全的前提下,建立了以能耗、正点、停车准确为目标的多目标列车节能优化操纵模型,提出并改进了模型的优化算法,进行了现场试验。论文的主要研究内容包括以下几个方面:1.以列车运动过程为基础,研究了列车运行过程中所受力的情况,分析了列车运行能耗的主要形态,通过理论分析和专家经验指出保持列车区间运行速度的均衡性和减少不必要制动是降低列车运行能耗的关键。建立了以能耗、运行时间和停车准确度为目标的列车运行过程优化模型。2.以遗传算法为基础研究列车优化操纵问题,并对优化算法进行改进。首先针对列车运行环境和操纵状态的不同,提出了基于混合编码的并行遗传算法,同时,为了加快算法的收敛速度,将机车驾驶员经验作为约束信息,融入到解的更新过程中,引导算法的寻优过程向最优解区域运动。将模拟退火算法用于求解列车优化操纵模型,经验证,计算结果能够满足列车运行控制要求。3.研究了列车节能操纵的相关方法,提出了将模拟退火算法结合遗传算法求解多目标列车优化操纵问题的方法,实际操纵列车与优化模型仿真对比结果表明,该算法具有较好的灵活性,不仅能适应不同的线路状况,同时,在满足安全、准点、停车准确的前提下,也能有效降低列车运行能耗。4.分析研究了列车的制动过程及操作要求,指出列车制动的关键是合理选择制动的初始点和缓解点,以及同时满足线路约束和避免二次制动的前提下尽可能减小列车动能损失。探讨了列车制动的控制变量及约束条件,建立了停车制动的模糊神经网络模型。仿真结果表明,采用模糊神经网络控制列车制动,能够在保证安全、平稳的前提下,实现列车的一次制动停车,有效避免了二次制动停车的控制方式,从而有利于降低列车运行能耗。本文从理论与实际应用两方面研究了列车优化操纵问题,建立了列车优化操纵模型,经现场实际操纵与仿真计算,该模型能有效降低列车运行能耗,对铁路行业的节能减排具有一定的理论意义和应用价值。
[Abstract]:The train operation is the result of the joint action of many factors in a complex and changeable environment. The train operation control in our country mainly depends on the locomotive driver's experience and operation technical level. Although the unit energy consumption of railway transportation is decreasing year by year, the total energy consumption is still huge. Therefore, it is of great significance to study the optimal operation of train energy saving for railway industry. In this paper, the optimal operation of train energy saving is studied from the two aspects of interval operation control and stop braking control. On the premise of ensuring the safety of train operation, the energy consumption and punctuality are established. This paper presents and improves the optimization algorithm of the multi-objective train energy saving operation model, and carries out the field test. The main contents of this paper include the following aspects: 1. Based on the train motion process, this paper studies the force acting on the train operation process, and analyzes the main forms of energy consumption in the train operation. Through theoretical analysis and expert experience, it is pointed out that the key to reduce the energy consumption of train operation is to maintain the equalization of train running speed and reduce unnecessary braking. The optimization model of train operation process is established, which aims at energy consumption, running time and stopping accuracy. Based on genetic algorithm (GA), train control problem is studied, and the optimization algorithm is improved. In order to speed up the convergence of the algorithm, the locomotive driver's experience is used as the constraint information to update the solution. The optimization process of the guidance algorithm moves towards the optimal solution region. The simulated annealing algorithm is used to solve the train control model. It is verified that the calculated results can meet the requirements of the train operation control. 3. In this paper, the related methods of train energy saving operation are studied, and the method of combining simulated annealing algorithm with genetic algorithm is proposed to solve the multi-objective train optimization problem. The simulation results show that the actual train operation is compared with the optimization model. The algorithm has good flexibility, not only can adapt to different line conditions, but also can effectively reduce the energy consumption of train operation. This paper analyzes the braking process and operation requirements of the train, and points out that the key of train braking is to reasonably select the initial braking point and the relief point, and to reduce the train kinetic energy loss as much as possible on the premise of satisfying the line constraints and avoiding the secondary braking. The control variables and constraint conditions of train braking are discussed, and the fuzzy neural network model of stopping braking is established. The simulation results show that using fuzzy neural network to control the train braking can realize the primary braking stop of the train on the premise of safety and stability, and effectively avoid the control mode of the secondary braking stop. Therefore, it is helpful to reduce the energy consumption of train operation. In this paper, the problem of train optimal operation is studied from both theoretical and practical aspects, and a train optimal operation model is established. After practical operation and simulation, the model can effectively reduce the energy consumption of train operation. It has certain theoretical significance and application value to the railway industry energy saving and emission reduction.
【学位授予单位】：北京交通大学
【学位级别】：博士
【学位授予年份】：2017
【分类号】：U268.6

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