An Inconsistency Fault Diagnosis Method for Lithium-Ion Cells in the Battery Pack Based on Piecewise Dimensionality Reduction and Outlier Identification

The inconsistency of cells in the battery pack is one of the main causes of battery failure. In practical applications, the terminal voltage is an important parameter that is easy to obtain and can characterize the inconsistency of cells. In this paper, a fault diagnosis method based on piecewise dimensionality reduction and outlier identification is proposed according to the voltage inconsistency of cells in the battery pack. This method uses a piecewise aggregate approximation (PAA) algorithm with a shift factor to reduce the dimension of the cell voltage time series, after which a deletion mechanism is designed based on the clustering algorithm and outlier identification to calculate the clustering quality after deleting each cell, reflecting the deviate degree of each cell. In addition, a safety management strategy is designed based on the Z-score method, and an abnormality coefficient is set to evaluate the inconsistency of cells. The effectiveness of the proposed diagnosis method is verified by monitoring the voltage data of two real-world electric vehicles. The verification results show that the method can not only detect the inconsistency before the failure of the faulty cell in the battery pack in advance, but also reduce the risk of computational explosion caused by the voltage time series and accurately locate the faulty cell.