## 4 Methodology
### Convolutional Neural Network with a Long Short-Term Memory (CNN-LSTM)-Based Classification Module  
To effectively classify the augmented power battery operation data, this study constructs a deep learning model that combines a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network. This hybrid model leverages the ability of CNN to extract local features and the LSTM strength in modeling temporal dependencies, enabling fault detection within multivariate battery charging time-series data. The overall architecture of the model is illustrated in Figure 7.  
The input format of this module is consistent with that used in the GAN model, namely, a multivariate time series with shape [256; 8]. Each sample contains 256 time steps and eight feature channels, corresponding to the following: voltage, current, State of Charge (SOC), maximum/minimum cell voltage, maximum/minimum cell temperature, and timestamp. The model outputs a probability value within the range [0, 1], indicating the likelihood that the sample represents a faulty charging process.  
The model architecture consists of two main components: a CNN-based feature extraction module and a BiLSTM-based temporal modeling module.  
In the CNN module, three sets of 1D convolutional layers (Conv1D) followed by max pooling operations are applied to progressively extract local temporal features from the input sequence. Each convolutional layer uses a kernel size of 3 and padding of 1 to capture local variations at different time scales--effectively detecting short-term patterns such as sudden voltage drops, SOC fluctuations, or abrupt temperature rises. Each convolutional layer is followed by a ReLU activation function to introduce non-linearity, and a max pooling operation to reduce the temporal dimension, thereby improving feature compactness and robustness.  
In the LSTM module, the three-dimensional feature tensor output by the convolutional layers is reshaped to match the input requirements of the LSTM network and then passed into a bidirectional LSTM (BiLSTM) layer. This structure captures both forward and backward temporal dependencies, enabling the model to learn sequential patterns such as SOC progression, voltage oscillations, and other evolving trends during the charging  
Figure 7: Architecture of the CNN-LSTM classification model.  
process. The final hidden states from both directions of the BiLSTM are concatenated to form a global temporal feature representation.  
The resulting temporal feature vector is fed into a two-layer fully connected network. The first layer applies non-linear compression and incorporates dropout regularization to reduce the risk of overfitting. The final layer uses a Sigmoid activation function to output a scalar probability indicating whether the current sample is classified as a fault. The specific network layer details and parameter settings for each layer of module are shown in Table 3.  
The model is trained on the balanced dataset augmented by the GAN, with input samples in the form of time series with shape (256, 8), and outputs representing the probability of each sample being either faulty or normal. The binary cross-entropy (BCE) loss function--consistent with that used in the GAN module--is employed for optimization. BCE loss is widely recognized as the standard loss for binary classification tasks, as it measures the divergence between the predicted probability distribution and the true labels, and provides well-behaved gradients that facilitate stable and efficient model training [28]. The Adam optimizer is selected, with an initial learning rate of 0.001, and the batch size is set to 64.  
In the subsequent experimental section, this model will be compared with other classification approaches to evaluate its performance in the task of power battery fault detection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline