Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact

ZENG Weijie; CUI Zezheng; YIN Huaping; SHEN Liman; LIU Mouhai; ZHENG Wenlin

doi:10.3969/j.issn.1008-0198.2025.04.002

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Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact

更新时间：2025-12-08

- Improved Decision Tree Prediction Method for Electric Vehicle Charging Pile Failure Considering Typhoon Impact
- Vol. 45, Issue 4, Pages: 10-14(2025)
- 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
  CLC： TM925.06
- Published Online：05 September 2025，
  
  Published：05 September 2025
- 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.

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references

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