## 5 Conclusions  
This study proposes the GATSCNN model to address the existing issues with current battery SOC estimation models, which include handling volatile data, uncertainty in neural network model parameters, lower precision caused by drastic fluctuations in experimental data for a battery SOC of below 20%, and decreased estimation accuracy caused by unusual data-processing methods after GA optimization. The GATSCNN model first converts raw experimental data into time-series data, and then uses GA optimization to determine the optimal network structure for the TSCNN model. Subsequently, by checking the function transformation of GA-optimized data that are unusual, the optimal network structure is determined and the accuracy of battery SOC estimation is verified. The experimental results indicate that the GATSCNN model has four advantages over existing BP, CNN, and RNN neural network models:  
1. Improved estimation effect when the battery SOC is below 20%. 2. More accurate SOC estimation by the TSCNN model, and thus stronger feature extraction capabilities for time-series data. 3. Combining GA with TSCNN to improve the accuracy of battery SOC estimation without changing the characteristics of the TSCNN model, thereby performing even better when the battery SOC is below 20%. The GA optimizes the number of convolutional layers, the number of neurons, the convolutional kernel length, and the batch size of the TSCNN model. It also optimizes the time-series length to match the optimal TSCNN model parameters, overcoming the difficulties and errors caused by the artificial setting of the network structure and parameters and thus improving battery SOC estimation. 4. Introducing a check function that can transform unusual GA-optimized data into normal data to increase data correlation and robustness, accelerate the convergence speed of the population, and improve optimization efficiency. In future work, an adaptive GA will be used to optimize the neural network structure and parameters, automatically adjusting the crossover and mutation probabilities according to the fitness function to find the optimal individual more quickly, thus improving the convergence speed and optimization ability of the GA. Second, the proposed method was applied to practical applications. The next step is to investigate how to integrate the method into a battery management system. Finally, additional optimization algorithms and deep learning neural networks should be explored for battery SOC estimation. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\(\geq\)**20\%**} & \multicolumn{3}{c}{**DST**} & \multicolumn{3}{c}{**UDDS**} \\  & & & **\textless{}20\%** & & \(\geq\)**20\%** & & & **\textless{}20\%** \\  & **MSE** & **Max** & **MSE** & **Max** & **MSE** & **Max** & **MSE** & **Max** \\  & **Error** & & & **Error** & & **Error** & & **Error** \\ \hline GABP & 4.58 & 15\% & 1.58 & 15\% & 4.44 & 8\% & 2.23 & 7\% \\ GACNN & 12.0 & 11.78\% & 3.34 & 11.64\% & 7.58 & 7.23\% & 4.83 & 7.21\% \\ GARN & 1.57 & 6.63\% & 0.68 & 5.06\% & 1.08 & 3.14\% & 0.95 & 2.73\% \\ GATSCNN & 1.29 & 4.55\% & 0.67 & 4.38\% & 0.68 & 2.58\% & 0.31 & 2.39\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Max error and MSE for SOC of different batteries on two datasets. **Author Contributions:** Conceptualization, J.L. and X.W. ; methodology, J.L. and X.W. ; software, J.L. and X.W. ; validation, X.W., J.L. and H.L. ; formal analysis, J.L. ; investigation, H.L. ; resources, J.L. ; data curation, X.W. ; writing--original draft preparation, X.W. and H.L. ; writing--review and editing, X.W. and J.L. ; visualization, X.W. ; supervision, J.L. ; project administration, J.L. ; funding acquisition, All authors have read and agreed to the published version of the manuscript.