| 基于列车运行时间偏差惩罚的高速列车节能优化方法 |
| |
| 引用本文： | 马阳阳, 孟学雷, 贾宝通, 任媛媛, 秦永胜. 基于列车运行时间偏差惩罚的高速列车节能优化方法[J]. 交通信息与安全, 2021, 39(4): 84-91. doi: 10.3963/j.jssn.1674-4861.2021.04.011 |
| |
| 作者姓名： | 马阳阳 孟学雷 贾宝通 任媛媛 秦永胜 |
| |
| 作者单位： | 兰州交通大学交通运输学院 兰州 730070 |
| |
| 基金项目： | 国家自然科学基金项目71861022 |
| |
| 摘 要： | 合理的安排列车在区间的运行方式能够有效的降低列车运行能耗。采用基于区间限速的列车工况确定策略确定列车区间运行工况, 以列车运行能耗为优化目标, 以列车运行距离、时间和列车限速等为约束条件, 在目标函数中加入列车运行时间偏差惩罚项, 建立基于列车运行时间偏差惩罚的高速铁路列车运行节能优化数学模型, 采用基于高斯变异和混沌扰动的改进人工蜂群算法对优化模型进行求解。以CRH3-350型动车组数据为例对模型与算法进行验证, 求解结果显示: 考虑列车运行时间偏差惩罚比不考虑列车运行时间偏差惩罚能耗可节省2.5%, 改进人工蜂群算法与基本人工蜂群算法、粒子群算法相比, 在目标值方面分别提高了4.2%和4.1%。采用基于区间限速的列车运行工况确定策略结合能耗优化模型能够满足不同限速和不同区间运行时分要求下的列车运行情况。表明所建模型和设计的算法有良好的求解效率和优化质量。
|
| 关 键 词： | 交通规划 高速铁路列车 时间偏差惩罚 节能优化 人工蜂群算法 |
| 收稿时间： | 2021-04-25 |
An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation |
| |
| MA Yangyang, MENG Xuelei, JIA Baotong, REN Yuanyuan, QIN Yongsheng. An Energy-saving Optimization Method of High-speed Trains Based on Time Deviation Penalty During Train Operation[J]. Journal of Transport Information and Safety, 2021, 39(4): 84-91. doi: 10.3963/j.jssn.1674-4861.2021.04.011 |
| |
| Authors: | MA Yangyang MENG Xuelei JIA Baotong REN Yuanyuan Qin Yongsheng |
| |
| Affiliation: | School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China |
| |
| Abstract: | Reasonable arranging the operation mode of the train in the section can reduce the energy consumption of train operation. A determination strategy of train operating conditions based on the speed limit of the interval is adopted to determine the operating condition of the train. The energy consumption of the train is used as the optimization objective, and the distance, time, and speed limit of the train are used as the constraints. Time deviation penalty during train operation is added to the objective function to develop a mathematical model of energy-saving optimization for high-speed railway train operation, and the improved artificial bee colony algorithm based on Gaussian mutation and chaotic disturbance is used to solve the optimization model. The model and algorithm are verified with CRH3-350 multi-unit data as an example, the solution results show that the energy consumption can be saved by 2.5% when time deviation penalty during train operation is considered. Compared with the basic artificial bee colony algorithm and particle swarm algorithm, the improved artificial bee colony algorithm has improved the target value by 4.2% and 4.1%, respectively. Adopting the determinative strategy based on the interval speed limit combined with the energy-consumption optimization model can meet the required train operation conditions under different speed limits and different intervals. It shows that the established model and the designed algorithm have good problem-solving efficiency and optimized quality. |
| |
| Keywords: | traffic regulation high-speed railway train time deviation penalty energy-saving optimization artificial bee colony algorithm |
| 本文献已被 CNKI 万方数据 等数据库收录！ |
| 点击此处可从《交通信息与安全》浏览原始摘要信息 |
|
点击此处可从《交通信息与安全》下载免费的PDF全文 |
|
