## 4 Experimental Analysis
### GA Experimental Verification
#### 4.2.1 Genetic Optimization for Experimental Analysis  
In Figure 16a and Table 4, in the BP neural network optimized by GA, the max error on DST and UDDS decreased by 2% and 1%, respectively. The MSE values also decreased by 2.29 and 1.7, respectively. This indicates that using the GA-optimized BP neural network significantly improves the accuracy of the SOC estimation.  
Figure 16: Comparison of errors before and after GA optimization for (**a**) BP, (**b**) CNN, (**c**) RNN, and (**d**) TSCNN.  
Figure 15: Max error comparison for battery SOC \(\geq\) 20% against SOC < 20% on (**a**) DST and (**b**) UDDS datasets.  
In Figure 16b and Table 4, it can be observed that in the GA-optimized CNN network, the max error was reduced by about 2.3% on both datasets and the MSE decreased by 3.21 and 2.23, respectively. This indicates that using the GA-optimized CNN significantly improves the accuracy of the SOC estimation.  
In Figure 16c and Table 4, it can be observed that in the GA-optimized RNN neural network the max error was reduced by 7.47% and 9.34% on DST and UDDS, respectively. The percentage of MSE decrease was also significant, at 19.64 and 19.07 percentage points, respectively. This indicates that GA optimization significantly improves the SOC estimation performance of the RNN neural network.  
Finally, in Figure 16d and Table 4, in the GA-optimized TSCNN neural network, the max error was reduced by 1.71% and 1.81% on the DST and UDDS datasets, respectively. However, the decrease in MSE was relatively small, at 1.51 and 2.92, respectively. Meanwhile, on the prediction set, the max errors achieved after GA optimization were 4.45% and 2.58%, respectively, demonstrating excellent SOC estimation performance. This indicates that GA optimization significantly improves the accuracy of SOC estimation for the TSCNN neural network.  
As shown in Figure 17, after using GA optimization, all of the networks achieved excellent SOC estimation performance. Among them, the network with the most significant improvement was RNN, followed by TSCNN. In particular, the GATSCNN network displayed the best performance on both datasets, achieving a max error optimization of 4.45% on DST and 2.58% on UDDS.  
The error radar chart shows the error of each data point in all test rounds, providing an intuitive understanding of the SOC estimation performance for each model. In comparing the results with those before GA optimization, it can be observed that GA optimization improved the accuracy of SOC estimation for all networks, and the improvement effect of GATSCNN was the most significant. Therefore, using GA optimization for neural networks is highly effective in SOC estimation tasks.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**MSE**} & **DST** & & **UDDS** \\  & & **Max Error** & **MSE** & **Max Error** \\ \hline GABP & 3.7 & 15\% & 3.9 & 8\% \\ GACNN & 9.5 & 11.8\% & 7.02 & 7.2\% \\ GARNN & 1.3 & 6.6\% & 1.05 & 3.1\% \\ GATSCNN & 1.12 & 4.45\% & 0.6 & 2.58\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of network effects after GA optimization.  
Figure 17: Radar chart of (**a**) DST and (**b**) UDDS datasets before and after GA optimization.