基于迭代诱导遗传算法的列车自适应节能策略研究

梅文庆; 史可; 张征方

doi:10.13890/j.issn.1000-128X.2023.03.015

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基于迭代诱导遗传算法的列车自适应节能策略研究

电力牵引与控制 | 更新时间：2024-08-02

- 基于迭代诱导遗传算法的列车自适应节能策略研究
- Train adaptive energy saving strategy based on iterative induced genetic algorithm
- 机车电传动 2023年第3期页码：117-123
- 作者机构：
  
  株洲中车时代电气股份有限公司，湖南株洲　　412001
- 作者简介：
  
  史　可（1993—），男，博士，工程师，主要从事列车自动驾驶算法研究；E-mail: 303477513@qq.com
- 基金信息：
  
  中国中车科技研究开发计划重点项目(2022CTB143)
- DOI：10.13890/j.issn.1000-128X.2023.03.015
  中图分类号： U268.7
- 纸质出版日期：2023-05-10，
  
  收稿日期：2022-12-13，
- 稿件说明：
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梅文庆, 史可, 张征方. 基于迭代诱导遗传算法的列车自适应节能策略研究[J]. 机车电传动, 2023(3): 117-123.

MEI Wenqing, SHI Ke, ZHANG Zhengfang. Train adaptive energy saving strategy based on iterative induced genetic algorithm[J]. Electric Drive for Locomotives,2023(3): 117-123.
梅文庆, 史可, 张征方. 基于迭代诱导遗传算法的列车自适应节能策略研究[J]. 机车电传动, 2023(3): 117-123. DOI： 10.13890/j.issn.1000-128X.2023.03.015.

MEI Wenqing, SHI Ke, ZHANG Zhengfang. Train adaptive energy saving strategy based on iterative induced genetic algorithm[J]. Electric Drive for Locomotives,2023(3): 117-123. DOI： 10.13890/j.issn.1000-128X.2023.03.015.

摘要

针对既有列车节能策略未合理考虑复杂操纵特性约束和动态牵引系统效率问题，文章基于牵引系统效率特性和轮周机械功构建列车运行能耗精细计算模型，提出基于迭代诱导遗传算法的自适应节能策略，设计考虑牵引系统效率的能耗目标函数，并在操纵特性约束下求解目标函数，实现最优节能规划。首先，根据列车线路坡道将优化目标设计为若干个子区间，将子区间的线路特征信息转化为状态约束和控制约束，提高了节能目标对线路信息变化下操纵特性的自适应性；然后，为克服传统遗传算法由于离散独立难以解决连续子区间优化问题的特点，设计迭代诱导遗传算法，在迭代诱导遗传算法中对节能目标函数计算Pareto最优解集；最后，使用MATLAB软件对算法进行仿真验证。不同线路信息的仿真结果表明，基于迭代诱导遗传算法的自适应节能策略在保证运行时间的条件下具有良好的节能性能，相比于恒速和恒力运行，节能指标提高3%~13%。

Abstract

In response to the inefficient consideration of complex manipulation characteristics constraints and dynamic traction system efficiency issues in existing train energy-saving strategies

a detailed energy consumption calculation model for train operation was proposed based on the efficiency characteristics of the traction system and the mechanical work of the wheel rotation. An adaptive energy-saving strategy based on an iterative induced genetic algorithm was presented

and an energy consumption objective function considering the traction system efficiency was designed. The objective function was solved under manipulation characteristic constraints to achieve optimal energy-saving planning. Firstly

the optimization objective was designed as several sub-intervals based on the ramp of the train route. The route characteristic information of the sub-intervals was transformed into state and control constraints

improving the adaptivity of the energy-saving objective to the manipulation characteristics under changes in route information. Then

to overcome the difficulty of traditional genetic algorithms in solving continuous sub-interval optimization problems due to discrete independence

an iterative induced genetic algorithm was designed to calculate the Pareto optimal solution set for the energy-saving objective function. Finally

the algorithm was simulated and validated on the MATLAB platform. The simulation results of different line information show that the adaptive energy-saving strategy based on iterative induced genetic algorithm has good energy-saving performance under the condition of ensuring running time

with an energy-saving index improvement of 3%-13% compared to constant speed and constant force operation.

关键词

节能策略复杂操纵特性约束牵引效率动态特性迭代诱导遗传算法高速列车

Keywords

energy saving strategyconstraints on complex manipulation characteristicsdynamic characteristics of traction efficiencyiterative inductiongenetic algorithmhigh-speed train

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