电动汽车串联型电弧故障检测方法

Series arc fault detection method in electric vehicle

电弧故障是引发电气火灾的主要原因之一。在电动汽车电气系统中,直流串联电弧故障通常发生在接触点松动或线路连接损坏处,会引起火灾、爆炸等严重事故。为快速、准确地检测电动汽车串联型电弧故障,搭建了电动汽车故障电弧实验平台,采集不同工况下干路电流时间序列并建立了样本库。通过轻量化卷积神经网络,建立了基于改进Mobilenet网络的串联故障电弧检测模型。通过对比分析学习率、网络层数、样本长度,对模型进行了优化。该优化模型通过干路电流可实现电动汽车串联型故障电弧的检测和故障选线,检测准确率达到96.39%,论文为电动汽车电气系统电弧故障检测提供了一种可行性方案。

Arc fault is one of the main causes of electrical fire.In the electric system of electric vehicle,the DC series arc fault usually occurs at the loose contact point or the line connection damage,which will cause serious accidents such as fire and explosion.In order to quickly and accurately detect the series electric arc fault of electric vehicles,an experimental platform for electric car fault arc has been constructed to collect time series data on trunk current under various operating situations and create a sample library.Through the lightweight convolutional neural network,a series fault arc detection model based on the improved Mobilenet network is established.By comparing and analyzing the learning rate,network layer number and sample length,the model is optimized.The optimization model can realize the detection of series fault arc,the selection of fault lines of electric vehicles through the trunk current,and the detection accuracy reaches 96.39%.This paper provides a feasible scheme for the arc fault detection of electric vehicle’s electrical system.