Voltage Correlation-Based Principal Component Analysis Method for Short Circuit Fault Diagnosis of Series Battery Pack

This article concerns the issue of data-driven fault diagnosis for series lithium-ion battery pack. A voltage correlation-based statistical analysis method is proposed. First, the voltage of each cell within the battery pack is measured independently, and the correlation coefficient (CC) between the voltages of adjacent cells is calculated. Then, all the CC signals under normal conditions are used to train a principal component analysis model, on the basis of which synthetic statistic and kernel density estimation-based thresholds are designed for simultaneously monitoring all the measured CC signals at each sampling instant. Once a fault is detected, an accumulative relative contribution plot algorithm is immediately used to isolate which CC signal has a problem and locate the faulty cell through a cross-positioning strategy. The experimental results on a realistic test platform for series battery pack show that the new method provides accurate and reliable assessments for different fault specifics, and it performs better than the state-of-the-art CC methods in terms of parallel processing capability, sensitivity to weak short circuit faults, and robustness to window width.