## 4 Experimental Analysis  
### Results of the Unoptimized Neural Network Experiments  
#### 4.1.1 Experimental Analysis of Neural Networks Prior to Optimization  
To train and test the accuracy of the neural network prediction, sample data were divided into training and validation sets. In total, 75% of the sample points were randomly selected from all the sample data as the training set and 25% as the validation set. This process was applied to both UDDS and DST discharge data.  
This study compared the performances of the CNN, BP, RNN, and TSCNN neural network models in estimating battery SOC, thereby validating the results for low battery levels. The effectiveness of the network architecture and the impact of the network depth on the accuracy of SOC estimation were evaluated. Network depth is an important factor that affects the performance of neural network models. In the experiments, by setting different network layers to 2, 4, and 6, the influence of network depth on model performance was explored. The specific comparison results are referred to as error fluctuations in Figure 12. By comparing the performances of the TSCNN, CNN, BP, and RNN models and setting different network depths, the advantages and disadvantages of different neural network models can be better assessed in handling drastic changes in current data, thereby providing a reference for practical applications.  
Based on the comparison of error fluctuations between the two datasets in Figure 12, it can be observed that different layer numbers correspond to different error fluctuations, indicating that the number of network layers had an impact on network performance. Therefore, it is necessary to adjust the number of neural network layers based on specific tasks and data characteristics to determine the optimal balance point and achieve a balance between learning ability, stability, and accuracy of the model.  
According to the data in Figure 13 and Table 2, the TSCNN network exhibited the best performance on both DST and UDDS datasets. On the DST dataset, the TSCNN network had a max error of 6.26% and an MSE of 2.63. On the UDDS dataset, the TSCNN network had a max error of 4.39% and an MSE of 3.52.  
In Figure 13, it can be observed that different networks had different max errors for different numbers of layer settings. For example, the optimal number of layers for the BP neural network was approximately four, whereas for other networks it was two. This is because different network structures, datasets, and specific tasks affect the choice of network layers; the layers cannot be simply determined by following general rules. Therefore, it can be concluded that, for the current experimental scenario and task, the TSCNN has a stronger feature extraction ability for time-series data and can more accurately estimate SOC. After considering the impact of the network layers, TSCNN achieved outstanding prediction results on different datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**DST**} & \multicolumn{2}{c}{**UDDS**} \\  & **MSE** & **Max Error** & **MSE** & **Max Error** \\ \hline BP & 5.99 & 17\% & 5.6 & 9\% \\ CNN & 12.71 & 14.1\% & 9.25 & 9.6\% \\ RNN & 20.94 & 14.1\% & 20.12 & 12.48\% \\ TSCNN & 2.63 & 6.26\% & 3.52 & 4.39\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test network effect verification.  
Figure 12: Error fluctuation charts for (**a**) DST and (**b**) UDDS datasets.  
Figure 13: Max error of the number of different layers of each neural network on the (**a**) DST and (**b**) UDDS datasets.