直流充电桩作为电动汽车有效的供电设备, 其故障频发对电动汽车充电安全带来隐患. 对充电桩的故障进行准确预测将有效地确保电动汽车充电过程的安全. 本文提出了一种改进门控循环单元(gate recurrent unit, GRU)直流充电桩的故障预测模型. 首先, 分析充电过程中直流充电桩的常见故障类型, 考虑到实际采集过程中具体故障数据样本量少的情况, 利用变分自编码器(variational auto-encoder, VAE)数据增强方法对样本数据进行扩充; 然后, 基于GRU网络模型的故障预测方法, 利用粒子群优化(particle swarm optimization, PSO)算法优化GRU网络参数, 采用支持向量机(support vector machine, SVM)模型改善网络输出的分类函数, 提出了PSO-GRU-SVM直流充电桩故障诊断模型; 最后, 利用算例对比改进前后的预测精度, 分析对比混淆矩阵热力图, 并且与常用的两种网络模型进行对比, 结果表明了文中方法有效的提高了预测精度, 验证了文章中方法的可行性.
Although direct-current (DC) charging piles are effective power supply equipment for electric vehicles (EVs), their frequent faults pose a threat to the charging safety of EVs. Accurately predicting charging pile faults can effectively ensure the safety of EVs in the charging process. For this reason, a fault prediction model for DC charging piles based on an improved gated recurrent unit (GRU) is proposed in this study. Specifically, the common fault types of DC charging piles during charging are analyzed. Considering the small sample size of specific fault data in the actual collection, variational autoencoder (VAE)-based data augmentation is performed to expand the sample data. Then, on the basis of the current fault prediction method based on the GRU network model, this study resorts to the particle swarm optimization (PSO) algorithm to optimize GRU network parameters, employs the support vector machine (SVM) model to improve the classification function output by the network, and thereby proposes a PSO-GRU-SVM fault diagnosis model for DC charging piles. Finally, an example is discussed to compare the prediction accuracy before and after the improvement, and the confusion matrix heatmaps are comparatively analyzed. Furthermore, the proposed model is compared with two commonly used network models. The results show that the proposed method can effectively improve prediction accuracy and thus verify the feasibility of the proposed method.