[1]徐 勇,刘 勇,戴计生,等.一种基于实时网络数据的列车蓄电池故障预警方法[J].控制与信息技术,2021,(02):107-111.[doi:10.13889/j.issn.2096-5427.2021.02.100]
 XU Yong,LIU Yong,DAI Jisheng,et al.A Fault Early Warning Method of Train BatteryBased on Real-time Network Data[J].High Power Converter Technology,2021,(02):107-111.[doi:10.13889/j.issn.2096-5427.2021.02.100]
点击复制

一种基于实时网络数据的列车蓄电池故障预警方法()
分享到:

《控制与信息技术》[ISSN:2095-3631/CN:43-1486/U]

卷:
期数:
2021年02期
页码:
107-111
栏目:
故障诊断
出版日期:
2021-04-05

文章信息/Info

Title:
A Fault Early Warning Method of Train BatteryBased on Real-time Network Data
文章编号:
2096-5427(2021)02-0106-06
作者:
徐 勇刘 勇戴计生张士强
(株洲中车时代电气股份有限公司,湖南株洲 412001)
Author(s):
XU Yong LIU Yong DAI Jisheng ZHANG Shiqiang
( Zhuzhou CRRC Times Electric Co., Ltd., Zhuzhou, Hunan 412001, China )
关键词:
列车蓄电池专家规则状态评估在线故障预警
Keywords:
train battery expert rule state assessment online fault early warning
分类号:
U279
DOI:
10.13889/j.issn.2096-5427.2021.02.100
文献标志码:
A
摘要:
蓄电池是列车降弓状态或紧急情况下的直接动力来源,对列车安全运行起着至关重要的作用。为防止因蓄电池故障导致机破及相关设备无法启动等现象的发生,文章基于列车蓄电池的工作原理及供电特性,提出一种监测数据与专家规则相结合应用的车载蓄电池在线故障预警方法。其在不额外增加传感器的前提下,通过分析既有列车多功能车辆总线(MVB)数据中与蓄电池工作状态相关的信号量随时间的演化特性,进行故障征兆特征提取与专家规则模型构建,实现蓄电池工作状态的实时评估与失效预警。目前该方法已被批量应用于某地铁线路车辆上,现场验证结果表明,该方法能够有效预警蓄电池故障,应用期间未发现漏报及误报现象。
Abstract:
Battery is a direct power source of the train under the condition of pantograph dropping or emergency, which plays an important role in the safety train operation. In order to prevent the occurrence of locomotive failure and related devices cannot startup caused by battery failure, combining with work principle and power supply characteristics, an online fault early warning method of train battery based on real-time monitoring data and expert rule is proposed. Under the premise of not deploying additional sensors, the time evolution property of signals related to battery working states in the real-time MVB data is analyzed, based on which, the warning fault features are extracted and expert rule module is constructed. Consequently, the real-time state assessment and fault early warning results are given. The method has been widely applied in train batteries of certain metro line, and no missing or false positives were found during the application, the verification results show the proposed method can effectively pre-warn battery fault.

参考文献/References:

[1] 石彩霞, 卢庆, 赵正虎. 城市轨道列车蓄电池选型分析[J]. 电力机车与城轨车辆, 2018, 41(3):82-85,91.

SHI C X, LU Q, ZHAO Z H. Analysis of Battery Type Selection for Urban Railway Train[J]. Electric Locomotives & Mass Transit Vehicles, 2018, 41(3): 82-85, 91.
[2] 张顺, 尹洪权, 吉敏. 城市轨道交通车辆蓄电池亏电故障分析及改善措施[J]. 城市轨道交通研究, 2019(11): 105-108.
ZHANG S, YIN H Q, JI M. Analysis of Battery Low-voltage Fault and Improvement Measures for Urban Rail Tran-sit Vehicle[J]. Urban Mass Transit, 2019,(11): 105-108.
[3] 郑耀. 广州地铁A型车镍铬碱性蓄电池的运用维护及应用现状[J].机电工程技术, 2017, 46(4): 149-152.
ZHENG Y. Maintenance and Application Status of Ni Cd Alkaline Battery in Guangzhou Metro Model A[J].Mechanical & Electrical Engineering Technology, 2017, 46(4): 149-152.
[4] 郭佑民, 胡广彭, 谢飞. 机车蓄电池在线监测与地面分析系统[J]. 仪表技术与传感器, 2012(8): 51-52.
GUO Y M, HU G P, XIE F. Online Monitoring and Ground Analysis System for Locomotive Batteries[J].Instrument Technique and Sensor, 2012(8): 51-52.
[5] CAI L, MENG J H, STROE D L, et al. Multiobjective Optimization of Data-Driven Model for Lithium-Ion Battery SOH Estimation With Short-Term Feature[J].IEEE Transactions on Power Electronics, 2020, 35(11): 11855-11864.
[6] VIDAL C, MALYSZ P, KOLLMEYER P, et al. Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art[J]. IEEE Access, 2020(8): 52796-52814.
[7] 高锋阳, 张国恒, 石岩, 等. 新型城轨电车混合动力系统能量管理策略[J].铁道学报, 2019, 41(4): 52-58.
GAO F Y, ZHANG G H, SHI Y, et al. Energy Management Strategy for New Type Urban Rail Vehicle Hybrid Power System[J]. Journal of the China Railway Society, 2019, 41(4): 52-58.
[8] 刘之奇. 基于蓄电池内阻在线监测系统对蓄电池性能诊断研究[J].贵州电力技术, 2017, 20(7): 19-23.
LIU Z Q. Research of the Battery Performance Diagnosis Based on Battery Internal Resistance On-line Monitoring System[J].Guizhou Electric Power Technology, 2017, 20(7): 19-23.
[9] 姜波, 李晓明. 城市轨道车辆车载蓄电池剩余容量估算方法[J]. 城市轨道交通研究, 2016(12): 66-68.
JIANG B, LI X M. Estimation of the Residual Capacity of Storage Batteries Installed on Metro Train[J]. Urban Mass Transit, 2016(12): 66-68.
[10] 戴银娟, 郭佑民, 高锋阳, 等. 基于粒子滤波算法的车载储能元件SOH预测方法研究[J]. 铁道科学与工程学报, 2019, 16(10): 2572-2577.
DAI Y J, GUO Y M, GAO F Y, et al. Research on SOH Prediction Method of Vehicle Energy Storage Element Based on Particle Filter Algorithm[J]. Journal of Railway Science and Engineering, 2019, 16(10): 2572-2577.

相似文献/References:

[1]徐 勇,刘 勇,戴计生,等.一种基于实时网络数据的列车蓄电池故障预警方法[J].控制与信息技术,2021,(02):1.[doi:10.13889/j.issn.2096-5427.2021.02.100]
 XU Yong,LIU Yong,DAI Jisheng,et al.An Fault Early Warning Method of Train Battery Based on Real-time Network Data[J].High Power Converter Technology,2021,(02):1.[doi:10.13889/j.issn.2096-5427.2021.02.100]

备注/Memo

备注/Memo:
收稿日期:2020-09-02
作者简介:徐勇(1985—),男,工程师,主要从事列车智能运维系统、故障预测与健康管理系统研发工作。
基金项目:国家重点研发计划(2016YFB1200401)
更新日期/Last Update: 2021-05-06