Train driving strategy optimization using control parameterization enhancing technique

Weifeng Zhong, Hongze Xu, Wenjing Zhang, Longsheng Wang
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引用次数: 2

Abstract

Train energy consumption accounts for the largest proportion of total energy consumption in railway systems. Applying the optimal driving strategy is an important way to reduce train energy consumption. In this paper, an efficient numerical approach - control parameterization enhancing technique (CPET) is employed to determine the optimal train driving strategy, which is essentially a problem of optimal control. Using CPET, the train control forces are indicated by piecewise constant function with variable switching nodes. Then, CPET transforms the original problem of optimal train control into a nonlinear optimization problem by considering both the piecewise constant control values on each subinterval and the lengths of the subintervals as decision parameters. Finally, the transformed optimization problem is solved efficiently by using an exact penalty method to handle the train speed constraint and applying a sensitivity approach to obtain the gradient of the cost function. A case study is carried out to demonstrate that the optimal driving strategy obtained by the CPET is more energy-efficient than that obtained by the traditional control parameterization method under same conditions.
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基于控制参数化增强技术的列车驾驶策略优化
列车能耗在铁路系统总能耗中所占比重最大。采用最优驾驶策略是降低列车能耗的重要途径。本文采用一种有效的数值方法-控制参数化增强技术(CPET)来确定列车的最优驾驶策略,这本质上是一个最优控制问题。利用CPET,列车控制力由分段常数函数表示,并带有可变的切换节点。然后,CPET将原最优列车控制问题转化为一个非线性优化问题,同时考虑每个子区间上的分段恒定控制值和子区间的长度作为决策参数。最后,采用精确惩罚法处理列车速度约束,采用灵敏度法求解代价函数梯度,有效地解决了变换后的优化问题。算例分析表明,在相同条件下,CPET得到的最优驾驶策略比传统控制参数化方法得到的更节能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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