基于相关系数特征提取的电池包内短路故障诊断

Short Circuit Fault Diagnosis in Battery Pack Based on Correlation Coefficient Feature Extraction

针对电池内短路特征不明显导致的故障难以检测的问题,提出了一种基于电压相关系数的故障特征提取方法。首先,计算电池组中相邻电池电压之间的改进递归相关系数;然后,利用经验模态分解算法,将得到的相关系数信号分解为多维特征指标,再通过核主成分分析对这些指标进行特征降维,得到最具代表性的故障特征;最后,使用支持向量机对所提取的成分进行建模,以检测和识别电池故障。在实验中,使用一个实际的电池测试平台来生成故障数据。实验结果表明,所提出的方法可以实现对内短路故障的准确检测,故障的检测率达到86.0465%。

Aiming at the problem that the internal short circuit fault characteristics of battery are not obvious,which makes it difficult to detect faults,a fault feature extraction method based on the correlation coefficient of voltage was proposed.Firstly,the improved recursive correlation coefficient between the voltages of adjacent cells in the battery pack is calculated;Then,the obtained correlation coefficient signals are decomposed into multidimensional feature indicators using an empirical modal decomposition algorithm,and these indicators are feature-dimensioned down by kernel principal component analysis to obtain the most representative fault features;Finally,the extracted components are modelled by using a support vector machine to detect and identify battery faults.In the experiment,an actual battery test platform was used to generate fault dataset.The experimental results show that the proposed method can achieve accurate detection of internal short circuit fault with a fault detection rate of 86.0465%.