重载智能驾驶列车空气制动力强弱评估方法研究

王飞宽; 蒋杰; 张征方; 罗源

doi:10.13890/j.issn.1000-128X.2022.05.016

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重载智能驾驶列车空气制动力强弱评估方法研究

电力牵引与控制 | 更新时间：2024-08-02

- 重载智能驾驶列车空气制动力强弱评估方法研究
- Research on strong and weak evaluation method of air brake for intelligent driving heavy haul train
- 机车电传动 2022年第5期页码：109-115
- 作者机构：
  
  1.国家能源集团包神铁路集团公司，内蒙古包头　　014010
  2.株洲中车时代电气股份有限公司，湖南株洲;412001
- 作者简介：
  
  王飞宽（1976—），男，工程师，主要从事铁路机车及自轮运转车辆等行车设备技术研究； E-mail: wangfeikuan@tom.com
- 基金信息：
- DOI：10.13890/j.issn.1000-128X.2022.05.016
  中图分类号： U292.92⁺1
- 纸质出版日期：2022-09-10，
  
  收稿日期：2021-10-11，
  
  修回日期：2022-08-10，
- 稿件说明：
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王飞宽, 蒋杰, 张征方, 等. 重载智能驾驶列车空气制动力强弱评估方法研究[J]. 机车电传动, 2022,(5):109-115.

WANG Feikuan, JIANG Jie, ZHANG Zhengfang, et al. Research on strong and weak evaluation method of air brake for intelligent driving heavy haul train[J]. Electric drive for locomotives, 2022,(5):109-115.
王飞宽, 蒋杰, 张征方, 等. 重载智能驾驶列车空气制动力强弱评估方法研究[J]. 机车电传动, 2022,(5):109-115. DOI： 10.13890/j.issn.1000-128X.2022.05.016.

WANG Feikuan, JIANG Jie, ZHANG Zhengfang, et al. Research on strong and weak evaluation method of air brake for intelligent driving heavy haul train[J]. Electric drive for locomotives, 2022,(5):109-115. DOI： 10.13890/j.issn.1000-128X.2022.05.016.

摘要

“3+0”编组的重载智能驾驶列车在循环空气制动控制过程中，由于空气制动力的大小受到线路条件、车辆载荷、闸瓦摩擦特性、管路差异性和空气制动间歇工作特性等多种因素影响，难以对列车进行准确规划和精准控制，甚至存在行车安全隐患。针对这一问题，文章提出一种基于改进粒子群-支持向量机（Improved Particle Swarm Optimization-Support Vector Machine

IPSO-SVM）的空气制动力强弱预测方法，采用IPSO对SVM的参数进行最优搜索，将影响空气制动力大小的主要因素作为SVM的输入，对空气制动力强弱进行评估，并基于神朔铁路现场实测数据进行了分析验证，验证结果显示文章所提出方法的预测精度可达90%以上，证明了该方法的合理性和有效性，验证结果表明该方法具有良好的实际工程应用价值。

Abstract

In the process of circulating air brake control of intelligent driving heavy haul train with the formation of 3+0

because the magnitude of air braking force is affected by various factors such as railway line conditions

vehicle load

friction characteristics of brake shoes

differences in pipelines

and intermittent working characteristics of air brakes

it is difficult to accurately plan and control trains

even causing potential safety hazards. To solve this problem

this paper proposed a prediction method for the strong and weak of air braking force based on IPSO-SVM (Improved Particle Swarm Optimization-Support Vector Machine). IPSO was used to optimize the parameters of SVM. The main factors affecting the magnitude of air braking force were used as the input of SVM to evaluate the strong and weak of air braking force

and the analysis and verification were carried out based on the measured data of Shenmu-Shuozhou railway. The results show that the prediction accuracy of the proposed method can reach more than 90%

which verifies the method is rational and effective

having good and practical value in engineering application.

关键词

重载列车智能驾驶空气制动改进粒子群支持向量机

Keywords

heavy haul trainintelligent drivingair brakingIPSOSVM

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