## 3 Data and Preprocessing  
The dataset used in this study was provided by a new energy vehicle manufacturer. It consists of power battery operation data collected during the charging phase from multiple vehicles of the same model. Notably, the dataset includes several battery samples that were retrospectively confirmed to have experienced faults. These faults were not successfully detected by the conventional Battery Management System (BMS) during actual operation, making the data particularly valuable for research purposes. The dataset contains a total of 28,389 samples, each represented as a multivariate time series with a shape of (256, 8). This indicates that eight key features are recorded across 256 time steps, namely, voltage, current, State of Charge (SOC), maximum cell voltage, minimum cell voltage, highest cell temperature, lowest cell temperature, and timestamp. Each sample is associated with a label: a label of 1 indicates that the vehicle subsequently experienced a fault (positive class), while a label of 0 denotes a normal charging process (negative class). This dataset realistically reflects the battery's operational status under diverse conditions and includes key samples in which faults went undetected by traditional BMS, providing a solid foundation for evaluating the effectiveness of deep learning methods in early fault diagnosis. Statistical analysis of the dataset reveals 23,735 normal (negative) samples and 4654 faulty (positive) samples, resulting in a class ratio of approximately 1:5. This pronounced class imbalance may bias classification models toward the majority class during training, impairing their ability to identify the critical minority class (i.e., fault samples). Therefore, to ensure effective learning and reliable fault identification, it is necessary to introduce appropriate data balancing strategies to mitigate the adverse effects of class imbalance. Subsequently, a data integrity check was performed on the raw dataset, and no missing values or formatting errors were detected. To eliminate the influence of differing physical scales among features, Z-score normalization was applied to all variables, enhancing feature comparability and accelerating model convergence. Given that power battery data represent a type of multivariate time series, analyzing the correlations among features is crucial for effective modeling. In this study, correlation analysis was conducted from two perspectives:  
1. intra-sample feature relationships, and
2. statistical correlations across the entire dataset. For intra-sample analysis, we selected one normal sample and one faulty sample and calculated the Pearson correlation coefficients among the eight time-series variables. As shown in Figure 1, both samples exhibit strong correlations among most features (such as voltage, current, and SOC), except for maximum/minimum cell temperature and timestamp. A closer inspection reveals differences in the correlation coefficients between the selected normal and faulty samples, for example, the correlation between current and SOC has changed, suggesting that faults may alter the inter-feature relationships in the battery data. To further examine whether battery faults lead to changes in inter-feature relationships, we computed the pairwise correlation coefficients for all features across the full sets of normal and faulty samples. The resulting correlation heatmaps, shown in Figure 2, reveal observable differences between the two groups, although most changes are relatively small in magnitude. We also performed statistical significance testing using the Fisher r-to-z transformation, and the results indicate that all feature pairs exhibit statistically significant differences in correlation coefficients between normal and faulty samples (\(p<0.001\)). This finding suggests that battery faults induce subtle but detectable structural changes in feature relationships.