Early micro-short circuit fault diagnosis of lithium battery pack based on Pearson correlation coefficient and KPCA

Abstract

Though lithium batteries have been extensively applied in electric vehicles as the power sources due to the excellent advantages in recent years, the safety risk of them is always existed inarguably. For the effect of early micro-short circuit faults on the electrical characteristics of the batteries is minor, it is hard to be detected and diagnosed timely, as a result it may evolve into direct short circuit and cause severe safety accidents, such as thermal runaway, explosion and so on. This paper proposes a method for diagnosing micro-short circuit fault in battery pack based on Pearson correlation coefficients (PCCs) and kernel principal component analysis (KPCA). Firstly, the voltage values of each battery in various operation states are measured and denoised using a moving filter method, then PCCs between each battery's voltage of the same series branch are calculated to form a dataset. Secondly, the dataset of PCCs is taken to train KPCA algorithm to obtain the control threshold by calculating the squared prediction error (SPE) statistic. After the training process finished, the PCCs of real-time voltage values of the batteries are calculated, then the related SPE statistic is calculated to determine whether the threshold is exceeded or not, meanwhile the contribution graph will be drawn if the threshold is exceeded. Finally, the faulty cell can be cross localized according to the feature of contribution rate in the contribution graph. Experiments are carried out under the condition of Dynamic Stress Test (DST), and comparisons are performed with principal component analysis (PCA) algorithm by using the different input features, such as raw voltage data, Kendall and Spearman correlation coefficient, respectively. The experimental results show that, the proposed method can diagnose the fault as soon as it occurred, and the feature of contribution rate is more obvious than other methods, which is beneficial to the fault localization. Moreover, for the fault judgment only depends on the correlation of the battery's voltages rather than the absolute values of them, which always vary obviously in different operation states, the proposed method can be applied in various engineering scenarios expediently.