南通职业大学学报2025，Vol.39Issue(4)：62-70,9.DOI:10.3969/j.issn.1008-5327.2025.04.012
基于GOA-SVM的电动汽车充电桩故障预测方法研究
A Fault Prediction Method for Electric Vehicle Charging Pile Based on GOA-SVM
张家俊 1陈芬 1马明 1陈杰1
作者信息
- 1. 南通开放大学 机电工程学院,江苏 南通 226006
- 折叠
摘要
Abstract
To address practical issues such as various types of fault charging piles and high difficulty in diagnosis,a fault prediction method is proposed based on the gazelle optimization algorithm(GOA)to optimize support vector machine(SVM)parameters.This method leverages GOA's global search capability to jointly optimize the penalty factor and kernel function parameters of SVM,thereby enhancing the model's prediction accuracy and generalization.Firstly,typical fault types of EV charging piles are systematically reviewed,and representative feature parameters are extracted.Then,a GOA-SVM prediction model is established and validated by comparative analysis of the results from multiple simulation experiments.The results show that the proposed method outperforms conventional SVM models in terms of fault detection,robustness and model convergence,demonstrating the effectiveness and feasibility of fault prediction for charging piles.This method offers robust technical support for the intelligent operation and maintenance of charging facilities,boasting considerable potential for engineering applications.关键词
电动汽车/充电桩/故障预测/支持向量机/瞪羚优化算法Key words
electric vehicle/charging pile/fault prediction/support vector machine/gazelle optimization algorithm分类
交通工程引用本文复制引用
张家俊,陈芬,马明,陈杰..基于GOA-SVM的电动汽车充电桩故障预测方法研究[J].南通职业大学学报,2025,39(4):62-70,9.
