城市轨道交通列车节能优化驾驶研究
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摘要
城市轨道交通系统以其运量大、安全、速达、舒适的优势,已经成为城市公共交通的主要组成部分。据中国城市轨道交通协会信息,至2012年8月底我国内地已有北京、上海、天津等15个城市累计开通了63条城市轨道交通运营线路,总运营里程达到1777公里。城市轨道交通系统运量庞大,随之而来的是巨大的能量消耗。如何减少城市轨道交通系统的能耗已经成为保持城市轨道交通可持续发展亟待解决的问题。
本文研究了城市轨道交通列车在两站之间的节能优化驾驶问题。首先通过极大值原理分析了列车在各种坡道上可能的运行状态,以及节能驾驶控制模式切换次数的上限,然后提出了节能驾驶轨迹分析方法来分析列车的节能驾驶控制模式的切换顺序,进而提出一般节能驾驶控制序列以及一般节能驾驶运行状态序列,最后在此基础上研究了新的单车节能优化驾驶方法和两车追踪节能优化驾驶方法。本文基于对列车节能驾驶轨迹的分析提出了节能驾驶轨迹优化方法,其中每个坡道上的节能驾驶轨迹最多通过4个决策变量就可以确定。鉴于城市轨道交通系统线路不长,坡道个数有限,所以建立的优化模型属于小规模非线性规划问题,非常适合采用序列二次规划方法进行快速求解。仿真算例表明本文提出的方法能够快速的生成节能驾驶曲线,在保证运行时间的情况下节约能耗。所以本文提出的方法即适用于列车节能驾驶曲线的离线规划,也适用于列车节能驾驶曲线的在线更新。
本文的主要创新点如下：
1.针对节能驾驶控制模式转换次数不明确的问题,基于极大值原理证明了列车在每一段坡度恒定的线路上节能驾驶时,节能控制模式切换次数的上限。同时得出了列车在某些坡道上不可能出现的节能驾驶模式。
2.针对极大值原理难以确定列车节能驾驶控制序列的问题,提出了一种基于几何分析的列车节能驾驶轨迹分析方法来分析列车的节能驾驶控制序列。轨迹分析方法中,针对不同的坡道提出了新的能耗模型,其中能耗的大小只与一个变量相关,所以可以方便的判断这个坡道上的最优节能驾驶轨迹及其相对应的控制模式,进而得到节能驾驶控制序列。采用这种方法得到的节能驾驶控制序列符合前人通过极大值原理得到的节能控制模式转换规律,体现了轨迹分析方法的可行性。
3.针对节能驾驶运行状态没有统一描述的问题,提出了一般节能驾驶控制序列及一般节能驾驶运行状态序列。通过轨迹分析的方法,详细分析了列车在各个子区段内的节能驾驶过程,得出了列车在每个子区段内的节能驾驶控制序列及其对应的运行状态,深入揭示了节能驾驶控制序列和运行状态变化的规律,并将他们描述为一个一般的形式。一般节能驾驶控制序列式由5种节能控制模式按顺序组成,一般节能驾驶运行状态序列由2种运行状态(变速、匀速)按顺序组成。
4.针对单车节能优化驾驶曲线难于的在线更新的问题,提出了新的城市轨道交通单车节能优化驾驶方法。将运行区间分为若干典型的子区间,根据一般节能优化驾驶控制序列和运行状态,考虑运行时间和距离等约束,将列车节能优化驾驶问题建模为小规模非线性规划问题。最后采用序列二次规划方法进行快速求解,不仅适用于列车节能驾驶曲线的离线规划,而且适用于列车在线调整,使列车减少能耗,并准点到达。
5.针对两车在站间追踪过程中节能驾驶问题不明确的问题,将前车的运行信息考虑到后车的节能驾驶中来,深入分析了移动闭塞条件下两车追踪的动态过程,发现了追踪运行的节能优化驾驶空间,提出了两车追踪运行情况下的节能驾驶场景。
6.针对两车在站间追踪运行时,后车不能在保证运行时间的情况下节约能耗的问题,提出了新的城市轨道交通列车追踪节能优化驾驶方法。根据两车追踪节能驾驶场景,提出后车追踪前车节能优化驾驶的过程就是在这几个场景中不断切换的过程。然后将这些场景归类为四个节能优化问题,并根据不同场景改进一般节能驾驶轨迹,并建立非线性规划模型。最后采用序列二次规划方法进行快速求解。当前车正常行驶时,后车可以追踪前车节能准点的到达下一站；当前车运行受到干扰使后车无法准点到站的情况下,可以使后车可以尽快到站并降低能耗。
Urban Rail Transit has become the major part of Urban Rail Transportation (URT) System with its safe and comfortable feature as well as its mass rapid transportation capability. Based on the statistics from China association of URT, there are over15cities have urban rail transit system, and the total number of operation lines exceed63until the end of August2012. The operating mileage reaches1777kilometers. Therefore, reducing energy consumption is a very important problem for sustainable development of URT.
In order to reduce energy consumption of URT trains, in this thesis, we research on energy-efficient optimization driving methods for trains within two successive stations. First, the possible energy-efficient control modes on different grades and the number of control modes switches are proved based on the maximum principle. Then a geometrical analysis based energy-efficient driving trajectory analysis method is proposed to analyze the control switching sequence. Through the trajectory analysis for different grades, general energy-efficient driving control sequence and running status sequence are proposed. Based on these, energy-efficient optimization methods for single train and two tracking trains are proposed, respectively. In our method, the energy-efficient driving trajectory for each gradient is determined by3or4decision parameters. Since the length of the distance between two successive stations is very short and the gradients are limited, all the optimization driving models we built belong to small-scale nonlinear programming problem, thus, it is particularly suitable for solving by sequence quadratic programming (SQP) algorithm. The simulation studies demonstrate that the energy consumption could be reduced and the solving time is very short, which means our new methods are not only suitable for energy-efficient trajectory's off-line per-planning, but also suitable for energy-efficient trajectory's online updating. The innovations of the dissertation are as follows:
1. Based on the maximum principle, the possible energy-efficient control modes on different grades and the number of control modes switches are proved.
2. Aiming to determine train energy-efficient control sequence, a geometrical analysis based train energy-efficient driving trajectory analysis method is proposed. In this new method, new energy consumption models for different gradients are designed in which only one variable is related to energy consumption. Thus, it is easy to determine the energy-efficient trajectory and the corresponding control sequence on each gradient.
3. Aiming to depict energy-efficient control modes and running status with a unified form, general energy-efficient driving control sequence and running status sequence are proposed. The general energy-efficient driving control sequence is composed by5control modes in order, and the general energy-efficient running status sequence is composed by2runing status (speed changing or holding) in order.
4. Aiming to update the train's energy-efficient trajectory online, a novel energy-efficient optimization driving method for a single train is proposed. In this method, energy consumption and running time ans distance can be easily formulated based on the general energy-efficient running status sequence, therefore, the energy-efficient driving optimization problem is built to a small-scale nonlinear programming problem which could be solved by SQP algorithm quickly.
5. The process of two trains'tracking between two stations is unclear. Aiming to solve this problem, the tracking dynamic process and the energy saving space for the following train are deeply analyzed with the consideration of the leading train's running information; energy-efficient driving scenarios for two trains'tacking are proposed.
6. Aiming to saving energy with the guarantee of running time, a novel energy-efficient driving optimization method for two tracking trains are proposed. The tracking process of the following train can be seen as a switching process among the energy-efficient driving scenarios. Then these scenarios are classified into4energy-efficient optimization problems, and the general energy-efficient control sequence and running status sequence are improved according to the different problems. After that, the whole tracking optimization process is modeled by four small-scale nonlinear programming problems according to different situations. It is particularly suitable for solving by SQP algorithm. Simulation results demonstrate the effectiveness of the proposed method.
本文研究了城市轨道交通列车在两站之间的节能优化驾驶问题。首先通过极大值原理分析了列车在各种坡道上可能的运行状态,以及节能驾驶控制模式切换次数的上限,然后提出了节能驾驶轨迹分析方法来分析列车的节能驾驶控制模式的切换顺序,进而提出一般节能驾驶控制序列以及一般节能驾驶运行状态序列,最后在此基础上研究了新的单车节能优化驾驶方法和两车追踪节能优化驾驶方法。本文基于对列车节能驾驶轨迹的分析提出了节能驾驶轨迹优化方法,其中每个坡道上的节能驾驶轨迹最多通过4个决策变量就可以确定。鉴于城市轨道交通系统线路不长,坡道个数有限,所以建立的优化模型属于小规模非线性规划问题,非常适合采用序列二次规划方法进行快速求解。仿真算例表明本文提出的方法能够快速的生成节能驾驶曲线,在保证运行时间的情况下节约能耗。所以本文提出的方法即适用于列车节能驾驶曲线的离线规划,也适用于列车节能驾驶曲线的在线更新。
本文的主要创新点如下：
1.针对节能驾驶控制模式转换次数不明确的问题,基于极大值原理证明了列车在每一段坡度恒定的线路上节能驾驶时,节能控制模式切换次数的上限。同时得出了列车在某些坡道上不可能出现的节能驾驶模式。
2.针对极大值原理难以确定列车节能驾驶控制序列的问题,提出了一种基于几何分析的列车节能驾驶轨迹分析方法来分析列车的节能驾驶控制序列。轨迹分析方法中,针对不同的坡道提出了新的能耗模型,其中能耗的大小只与一个变量相关,所以可以方便的判断这个坡道上的最优节能驾驶轨迹及其相对应的控制模式,进而得到节能驾驶控制序列。采用这种方法得到的节能驾驶控制序列符合前人通过极大值原理得到的节能控制模式转换规律,体现了轨迹分析方法的可行性。
3.针对节能驾驶运行状态没有统一描述的问题,提出了一般节能驾驶控制序列及一般节能驾驶运行状态序列。通过轨迹分析的方法,详细分析了列车在各个子区段内的节能驾驶过程,得出了列车在每个子区段内的节能驾驶控制序列及其对应的运行状态,深入揭示了节能驾驶控制序列和运行状态变化的规律,并将他们描述为一个一般的形式。一般节能驾驶控制序列式由5种节能控制模式按顺序组成,一般节能驾驶运行状态序列由2种运行状态(变速、匀速)按顺序组成。
4.针对单车节能优化驾驶曲线难于的在线更新的问题,提出了新的城市轨道交通单车节能优化驾驶方法。将运行区间分为若干典型的子区间,根据一般节能优化驾驶控制序列和运行状态,考虑运行时间和距离等约束,将列车节能优化驾驶问题建模为小规模非线性规划问题。最后采用序列二次规划方法进行快速求解,不仅适用于列车节能驾驶曲线的离线规划,而且适用于列车在线调整,使列车减少能耗,并准点到达。
5.针对两车在站间追踪过程中节能驾驶问题不明确的问题,将前车的运行信息考虑到后车的节能驾驶中来,深入分析了移动闭塞条件下两车追踪的动态过程,发现了追踪运行的节能优化驾驶空间,提出了两车追踪运行情况下的节能驾驶场景。
6.针对两车在站间追踪运行时,后车不能在保证运行时间的情况下节约能耗的问题,提出了新的城市轨道交通列车追踪节能优化驾驶方法。根据两车追踪节能驾驶场景,提出后车追踪前车节能优化驾驶的过程就是在这几个场景中不断切换的过程。然后将这些场景归类为四个节能优化问题,并根据不同场景改进一般节能驾驶轨迹,并建立非线性规划模型。最后采用序列二次规划方法进行快速求解。当前车正常行驶时,后车可以追踪前车节能准点的到达下一站；当前车运行受到干扰使后车无法准点到站的情况下,可以使后车可以尽快到站并降低能耗。
Urban Rail Transit has become the major part of Urban Rail Transportation (URT) System with its safe and comfortable feature as well as its mass rapid transportation capability. Based on the statistics from China association of URT, there are over15cities have urban rail transit system, and the total number of operation lines exceed63until the end of August2012. The operating mileage reaches1777kilometers. Therefore, reducing energy consumption is a very important problem for sustainable development of URT.
In order to reduce energy consumption of URT trains, in this thesis, we research on energy-efficient optimization driving methods for trains within two successive stations. First, the possible energy-efficient control modes on different grades and the number of control modes switches are proved based on the maximum principle. Then a geometrical analysis based energy-efficient driving trajectory analysis method is proposed to analyze the control switching sequence. Through the trajectory analysis for different grades, general energy-efficient driving control sequence and running status sequence are proposed. Based on these, energy-efficient optimization methods for single train and two tracking trains are proposed, respectively. In our method, the energy-efficient driving trajectory for each gradient is determined by3or4decision parameters. Since the length of the distance between two successive stations is very short and the gradients are limited, all the optimization driving models we built belong to small-scale nonlinear programming problem, thus, it is particularly suitable for solving by sequence quadratic programming (SQP) algorithm. The simulation studies demonstrate that the energy consumption could be reduced and the solving time is very short, which means our new methods are not only suitable for energy-efficient trajectory's off-line per-planning, but also suitable for energy-efficient trajectory's online updating. The innovations of the dissertation are as follows:
1. Based on the maximum principle, the possible energy-efficient control modes on different grades and the number of control modes switches are proved.
2. Aiming to determine train energy-efficient control sequence, a geometrical analysis based train energy-efficient driving trajectory analysis method is proposed. In this new method, new energy consumption models for different gradients are designed in which only one variable is related to energy consumption. Thus, it is easy to determine the energy-efficient trajectory and the corresponding control sequence on each gradient.
3. Aiming to depict energy-efficient control modes and running status with a unified form, general energy-efficient driving control sequence and running status sequence are proposed. The general energy-efficient driving control sequence is composed by5control modes in order, and the general energy-efficient running status sequence is composed by2runing status (speed changing or holding) in order.
4. Aiming to update the train's energy-efficient trajectory online, a novel energy-efficient optimization driving method for a single train is proposed. In this method, energy consumption and running time ans distance can be easily formulated based on the general energy-efficient running status sequence, therefore, the energy-efficient driving optimization problem is built to a small-scale nonlinear programming problem which could be solved by SQP algorithm quickly.
5. The process of two trains'tracking between two stations is unclear. Aiming to solve this problem, the tracking dynamic process and the energy saving space for the following train are deeply analyzed with the consideration of the leading train's running information; energy-efficient driving scenarios for two trains'tacking are proposed.
6. Aiming to saving energy with the guarantee of running time, a novel energy-efficient driving optimization method for two tracking trains are proposed. The tracking process of the following train can be seen as a switching process among the energy-efficient driving scenarios. Then these scenarios are classified into4energy-efficient optimization problems, and the general energy-efficient control sequence and running status sequence are improved according to the different problems. After that, the whole tracking optimization process is modeled by four small-scale nonlinear programming problems according to different situations. It is particularly suitable for solving by SQP algorithm. Simulation results demonstrate the effectiveness of the proposed method.
引文
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