## 5 Experiments and Results  
### Experimental Setup and Evaluation Metrics  
To validate the effectiveness of the proposed GAN-based data augmentation method and the CNN-LSTM classification model in power battery fault detection, a series of experiments were conducted using a real-world dataset collected from new energy vehicles. All experiments follow a standard train/validation/test split strategy to ensure stable training and comprehensive evaluation of model performance. All experiments were implemented using the PyTorch 2.4.1 framework and conducted on a workstation equipped with an NVIDIA GPU. Prior to training, the original dataset was divided into three subsets: train set, validation set, test set. The test set remained untouched during both training and data augmentation, preserving the original data distribution for fair and objective performance evaluation. From the training set, all normal samples and labeled fault samples were extracted separately. The fault samples were used as real anomaly data to train the GAN model, enabling it to learn the latent distribution of abnormal sequences. Subsequently, the trained generator was used to produce synthetic fault samples, increasing the number of fault instances to approximately match the number of normal samples, thereby achieving class balance. Finally, the synthetic fault samples, real fault samples, and normal samples were combined to form a balanced training set, which was used to train the CNN-LSTM classification model. During training, the loss value and accuracy metrics were monitored in real-time on the validation set to guide decisions on the number of training epochs and optimization strategy, helping to prevent overfitting. After training, the model was evaluated on the test set to assess its final performance. To comprehensively evaluate the classification performance of the model, four commonly used binary classification metrics are adopted: Accuracy, Precision, Recall, and F1-Score. Accuracy measures the proportion of correctly predicted samples among all samples. Precision evaluates the proportion of correctly identified fault samples among all samples predicted as faulty. Recall assesses the proportion of actual fault samples that are successfully detected by the model. F1-Score is the harmonic mean of precision and recall, serving as an important metric for balanced performance evaluation. The mathematical definitions of these metrics are as follows Equations (5)-(8):  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{5}\]  
\[Precision=\frac{TP}{TP+FP} \tag{6}\]  
\[Recall=\frac{TP}{TP+FN} \tag{7}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{8}\]  
where:  
* \(TP\) (True Positive): the number of fault samples correctly predicted as faulty. * \(TN\) (True Negative) the number of normal samples correctly predicted as normal. * \(FP\) (False Positive): the number of normal samples incorrectly predicted as faulty. * \(FN\) (False Negative): the number of fault samples incorrectly predicted as normal. In imbalanced classification tasks, models tend to learn the characteristics of the majority class (i.e., normal samples) more easily, often leading to high accuracy for normal samples but poor detection of fault samples. Given that this study focuses on identifying abnormal battery behavior, the Precision, Recall, and F1-Score of the positive class (fault samples) are considered the core evaluation metrics, reflecting the model's practical effectiveness in fault detection. Fault Detection Experiments Based on the Generative Adversarial Network with a Convolutional Long Short-Term Memory (GAN-CNN-LSTM)  
To evaluate the effectiveness of the proposed GAN-CNN-LSTM-based method for power battery fault detection, we first divided the full dataset into training, validation, and test sets in a 6:2:2 ratio. The test set remained fixed throughout the experiments and was not subjected to any data augmentation. After splitting, the training set contained 17,033 samples, and the validation set contained 5678 samples.