## 5 Experiments and Results
### Overall Performance Comparison of Models  
To more intuitively demonstrate the performance advantages of the proposed GAN-CNN-LSTM model in the task of power battery fault detection, we present a consolidated summary of the key experimental results. As shown in Figure 14, the GAN-CNN-LSTM model achieved the highest scores across all three key metrics--Precision, Recall, and F1-Score--significantly outperforming both the other deep learning models and traditional machine learning models. A comparison between deep learning and traditional machine learning models reveals that, although certain machine learning methods (e.g., Random Forest and KNN) show some classification capability for structured battery operation data, their overall detection performance remains noticeably lower than that of deep learning models capable of directly modeling time-series features. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Label** & **Precision** & **Recall** & **F1-Score** & **Accuracy** \\ \hline \multirow{2}{*}{Random Forest} & 0 & 93.99\% & 97.62\% & 95.77\% & \multirow{2}{*}{92.78\%} \\  & 1 & 84.73\% & 67.86\% & 75.36\% & \\ \hline \multirow{2}{*}{Logistic Regression} & 0 & 90.26\% & 94.76\% & 92.46\% & \multirow{2}{*}{87.06\%} \\  & 1 & 63.76\% & 47.40\% & 54.38\% & \\ \hline \multirow{2}{*}{Naive Bayes} & 0 & 90.21\% & 59.51\% & 71.71\% & \multirow{2}{*}{60.69\%} \\  & 1 & 24.27\% & 66.77\% & 35.60\% & \\ \hline \multirow{2}{*}{KNN} & 0 & 95.73\% & 95.69\% & 95.71\% & \multirow{2}{*}{92.81\%} \\  & 1 & 77.86\% & 98.03\% & 77.95\% & \\ \hline \multirow{2}{*}{SVM} & 0 & 94.26\% & 92.85\% & 93.36\% & \multirow{2}{*}{89.46\%} \\  & 1 & 81.26\% & 51.75\% & 63.05\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative classification results of machine learning models. The results of the ROC curves further support this trend. All deep learning models achieved higher Area Under the Curve (AUC) values than their machine learning counterparts. Notably, the GAN-CNN-LSTM model reached an AUC of 0.99, the highest among all models tested, demonstrating its outstanding discrimination capability in the fault detection task. To quantitatively evaluate whether the proposed GAN-CNN-LSTM model significantly outperforms baseline methods, we performed statistical significance testing on the Area Under the ROC Curve (AUC) using the bootstrap resampling method (n = 1000). As shown in Table 7, for each comparison between GAN-CNN-LSTM and the baseline models--including CNN-LSTM, CNN, LSTM, Random Forest, Logistic Regression, Naive Bayes, KNN, and SVM--the mean difference in AUC, along with its 95% confidence interval, was calculated. For all comparisons, the mean AUC difference was positive, and the 95% confidence intervals did not include zero. For example, the AUC of GAN-CNN-LSTM exceeds that of CNN-LSTM by an average of 0.018 (95% CI: 0.015-0.022), and exceeds Random Forest by 0.034 (95% CI: 0.029-0.039). The improvement over Naive Bayes is even larger, with a mean difference of 0.338 (95% CI: 0.323-0.355). These results demonstrate that the proposed model achieves robust and significant gains in fault detection performance compared with both deep learning and classical machine learning baselines. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Mean\_Diff** & **CI\_Lower** & **CI\_Upper** \\ \hline CNN-LSTM & 0.018 & 0.015 & 0.022 \\ CNN & 0.019 & 0.015 & 0.023 \\ LSTM & 0.027 & 0.023 & 0.031 \\ Random Forest & 0.034 & 0.029 & 0.039 \\ Logistic Regression & 0.116 & 0.105 & 0.127 \\ Naive Bayes & 0.338 & 0.323 & 0.355 \\ KNN & 0.043 & 0.037 & 0.052 \\ SVM & 0.116 & 0.103 & 0.130 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Bootstrap test results. Figure 16: Receiver Operating Characteristic (ROC) curves of all model. In summary, the proposed GAN-CNN-LSTM model, enhanced through GAN-based data augmentation, exhibits clear advantages in handling structured time-series data.