提高铁路驾驶效率:开发ecos(能耗优化软件)工具

F. Ariza, J. Mera, E. Castellote, I. Gómez-Rey
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引用次数: 0

摘要

铁路运输每年消耗大量的能源和燃料。环境问题日益严重,柴油和电力价格不断上涨,导致铁路系统必须采用优化技术。本文旨在解释一个软件工具的开发,该软件工具能够在给定的拓扑结构和时间表内计算列车服务的最佳消耗速度剖面。它还应提供驾驶员应该遵循的时间和动作(动力,制动,保持速度或滑行)来实现它。从历史上看,这些目标是使用基于非常简化的模型或动态规划的计算机算法来实现的,这导致了不现实或限制缓慢的框架。我们提出了一种基于最优控制理论的快速准确的方法。该问题被表述为一个微分方程组,表示列车动力学约束的轨道特征,如允许的速度或斜率值在各个路段。由于这类方程组具有严格的非线性,不适合解析解,必须采用快速收敛迭代方法,如基于对数障碍罚函数的内点法。使用该工具对几个实际场景进行了测试,并将其输出与验证结果进行了比较,实现了高度的准确性和速度。因此,该方法可用于实际列车的驾驶员咨询系统的开发。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ENHANCED EFFICIENCY IN RAILWAY DRIVING: DEVELOPMENT OF ECOS (ENERGY CONSUMPTION OPTIMIZATION SOFTWARE) TOOL
Railway transport consumes large amount of energy and fuel every year. Increasing environmental concern and raising prices in diesel fuel and electrical energy has resulted in optimization techniques being a must for railway systems. This paper aims to explain the development of a software tool able of calculating the optimal consumption speed profile for a train service within a given topology and timetable. It also shall provide when and what actions (powering, braking, holding speed or coasting) the driver should follow for achieving it. Historically these objectives have been approached using computer algorithms based on very simplified models or dynamic programming, resulting in unrealistic or restrictively slow frameworks. We propose a really fast and accurate methodology based on Optimal Control theory. The problem is formulated as a differential system of equations stating train dynamics constraint by track features, as permitted speed or slope value in the various sections. Analytical solution for such a system of equations is not suitable due to its strictly non-linearity, being necessary the application of fast convergence iterative methods as the Interior Point method based on logarithm barrier penalty functions. Several real-life scenarios have been tested using the explained tool and its output have been compared with validated results, having achieved a high degree of accuracy and speed. Accordingly is valid to conclude that this approach can be used to develop a Driver Advisory System for real trains.
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