考虑台风影响的电动汽车充电桩故障改进决策树预测方法

曾伟杰; 崔泽政; 尹华平; 申丽曼; 刘谋海; 郑闻麟

doi:10.3969/j.issn.1008-0198.2025.04.002

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考虑台风影响的电动汽车充电桩故障改进决策树预测方法

专家专栏：电力防灾减灾 | 更新时间：2025-12-08

- 考虑台风影响的电动汽车充电桩故障改进决策树预测方法
- Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact
- 湖南电力 2025年45卷第4期页码：10-14
- 作者机构：
  
  1. 国网湖南省电力有限公司,湖南,长沙,410004
  2. 智能电气量测与应用技术湖南省重点实验室,湖南,长沙,410007
  3. 长沙理工大学电气与信息工程学院,湖南,长沙,410114
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.04.002
  中图分类号： TM925.06
- 网络出版：2025-09-05，
  
  纸质出版：2025-09-05
- 稿件说明：
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曾伟杰, 崔泽政, 尹华平, 申丽曼, 刘谋海, 郑闻麟. 考虑台风影响的电动汽车充电桩故障改进决策树预测方法[J]. 湖南电力, 2025, 45(4): 10-14.

ZENG Weijie, CUI Zezheng, YIN Huaping, et al. Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact[J]. 2025, 45(4): 10-14.
曾伟杰, 崔泽政, 尹华平, 申丽曼, 刘谋海, 郑闻麟. 考虑台风影响的电动汽车充电桩故障改进决策树预测方法[J]. 湖南电力, 2025, 45(4): 10-14. DOI： 10.3969/j.issn.1008-0198.2025.04.002.

ZENG Weijie, CUI Zezheng, YIN Huaping, et al. Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact[J]. 2025, 45(4): 10-14. DOI： 10.3969/j.issn.1008-0198.2025.04.002.

摘要

为提升电动汽车充电桩在台风天气下的运行可靠性

提出一种融合台风影响因子的改进充电桩故障预测方法。首先对充电桩数据进行预处理操作。然后针对传统C4.5决策树算法在特征耦合效应下的预测性能不足问题

引入气象属性

并基于信息增益比优化特征选择策略

降低属性间相关性对分类准确性的干扰。算例结果分析表明

相比于传统算法

该算法的准确率提升了21%个百分点（相对提升30.1%）证实了台风影响因子的引入可优化预测模型性能。

Abstract

In order to improve the operational reliability of electric vehicle charging piles under typhoon weather

an improved charging pile fault prediction method integrating typhoon influence factors is proposed. First

the charging pile data is preprocessed. Secondly

in view of the insufficient prediction performance of the traditional C4.5 decision tree algorithm under the feature coupling effect

meteorological attributes are introduced and the feature selection strategy is optimized based on the information gain ratio to reduce the interference of inter-attribute correlation on classification accuracy. Finally

the analysis of the example results shows that the accuracy of this algorithm is improved by 21% compared with the traditional algorithm

which verifies the optimization effect of the introduction of typhoon influence factors on the performance of the prediction model.

关键词

Keywords

references

KHORASANI E S.Artificial intelligence:Structures and strategies for complex problem solving[J]. Scalable Computing:Practice and Experience,2008,9(3).

GALAGEDARAGE DON M,KHAN F.Process fault prognosis using hidden Markov model-Bayesian networks hybrid model[J]. Industrial & Engineering Chemistry Research,2019,58(27):12041-12053.

DU X W,XU G N,LI Z J,et al.Remaining useful life prediction of Lithium-ion batteries of stratospheric airship by model-based method[J]. Microelectronics Reliability,2019,100:113400.

CHU A,ALLAM A,CORDOBA ARENAS A,et al.Stochastic capacity loss and remaining useful life models for lithium-ion batteries in plug-in hybrid electric vehicles[J]. Journal of Power Sources,2020,478:228991.

柯赟,宋恩哲,姚崇,等. 船舶柴油机故障预测与健康管理技术综述[J]. 哈尔滨工程大学学报,2020,41(1):125-131.

ESKANDARNIA E,AL-AMMAL H,KSANTINI R,et al.Deep learning techniques for smart meter data analytics:a review[J]. SN Computer Science,2022,3(3):243.

吴达雷,梁树华,陈长基,等. 基于多维赋权威布尔分布的智能电表可靠性评价方法[J]. 西南大学学报(自然科学版),2024,46(4):164-173.

HUANG M T,ZHANG Q B.Prediction of remaining useful life of lithium-ion battery based on UKF[C]//2020 Chinese Automation Congress(CAC). Shanghai,China. IEEE,2020:4502-4506.

TSCHORA L,PIERRE E,PLANTEVIT M,et al.Electricity price forecasting on the day-ahead market using machine learning[J]. Applied Energy,2022,313:118752.

RAWAL B,AGARWAL R.Improving accuracy of classification based on C4.5 decision tree algorithm using big data analytics[M]//Computational Intelligence in Data Mining.Singapore:Springer Singapore,2018:203-211.

卢峭峰,叶魏涛,杨遂军,等. 基于D-S证据理论的多传感器燃爆判别方法[J]. 传感技术学报,2025,38(1):122-127.

张志勇,王宇翔,黄彩霞,等. 融合灰色预测和卡尔曼滤波的车辆侧向碰撞预警[J]. 机械工程学报,2024,60(20):240-250.

XIONG R,LI L L,LI Z R,et al.An electrochemical model based degradation state identification method of Lithium-ion battery for all-climate electric vehicles application[J]. Applied Energy,2018,219:264-275.

张吉宣,贾建芳,曾建潮. 电动汽车供电系统锂电池剩余寿命预测[J]. 电子测量与仪器学报,2018,32(3):60-66.

刘梓强,金涛,刘宇龙,等. 基于张量重构融合诊断的电动汽车直流充电桩开路故障诊断方法[J]. 中国电机工程学报,2023,43(5):1831-1843.

加鹤萍,郭宇辰,马乾鑫,等. 基于机器学习方法的现货电价预测研究综述[J]. 电力建设,2025,46(2):160-179.

ZHANG Y,YAN J.Crossformer:Transformer utilizing cross-dimension dependency for multivariate time series forecasting[C]//The eleventh international conference on learning representations. 2023.

LIU M,ZENG A,CHEN M,et al.Scinet:Time series modeling and forecasting with sample convolution and interaction[J]. Advances in Neural Information Processing Systems,2022,35:5816-5828.

于东民,杨超,蒋林洳,等. 电动汽车充电安全防护研究综述[J]. 中国电机工程学报,2022,42(6):2145-2164.

马丽叶,王海锋,卢志刚,等. 计及相关性影响的增强台风灾害下配电网韧性灵活性资源规划[J]. 电力系统自动化,2022,46(7):60-68.

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