# Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP
## 5 Conclusions  
In this study, parameters were determined using the genetic algorithm. GA minimizes time loss caused by trial-and-error methods to find optimal results. Moreover, its successful performance in prediction results enhances reliability by providing high accuracy. As evident in the prediction graphs and performance parameters, the CNN algorithm generally outperforms other algorithms across all batteries and training datasets. The highest accuracy achieved was 99.88% (1-RMSE), with 100 training data for battery B0005. The proposed method demonstrates superiority over the literature studies for three out of four batteries, underscoring the accuracy and reliability of this study. However, the lower prediction performance of battery B0006 compared to the literature studies highlights the challenges in predicting measurement results or electrochemical structures of this specific battery using methods like artificial neural networks and deep learning. In conclusion, this study, which provides the opportunity to predict with high accuracy even with low-training datasets, can be used in battery health and capacity prediction. In addition to the studies, the parameter optimization of the algorithms can be conducted in the future using different optimization methods. Moreover, optimization with genetic algorithms can be combined with different algorithms to further improve the prediction accuracy. Conceptualization, F.D. and S.K. ; methodology, F.D. and S.K. ; software, F.D. ; validation, F.D. and S.K. ; formal analysis, F.D. ; investigation, F.D. and S.K. ; resources, F.D. ; data curation, F.D. ; writing--original draft preparation, F.D. ; writing--review and editing, S.K. ; visualization, S.K. ; supervision, S.K. ; project administration, S.K. ; funding acquisition, S.K. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **References** & **Num. of Train** & **Prediction** & **Method** & **RMSE (\%)** & **R2** \\ \hline \multirow{8}{*}{**NASA(B0005)**} & Proposed & 100 & Capacity & GA-CNN & **0.1176** & **0.9996** \\  & [29] & 100 & SOH & CL-GraphSAGE & 0.5808 & 0.958309 \\  & [30] & 100 & RUL & GPR-GMR & 0.65 & - \\  & [37] & 85 & Capacity & VMD-PF-GPR & 1.28 & - \\  & [31] & \%60 & Residual life & CNN-LSTM & 0.9883 & - \\  & [33] & 98 & SOH & CNN-BiLSTM-AM & 0.481 & - \\  & [32] & 100 & Capacity & VMD-PF-ARIMA & 0.2562 & - \\  & [38] & 84 & Capacity & SVMD-AO-DELM & 0.75 & 0.9968 \\ \hline \multirow{8}{*}{**NASA(B0006)**} & Proposed & 100 & Capacity & GA-CNN & 4.4784 & 0.7991 \\  & [29] & 100 & SOH & CL-GraphSAGE & 1.0859 & 0.924613 \\  & [30] & 100 & RUL & GPR-GMR & 1.48 & - \\  & [31] & \%60 & Residual life & CNN-LSTM & 0.42671 & - \\  & [33] & 86 & SOH & CNN-BiLSTM-AM & 1.05 & - \\  & [32] & 100 & Capacity & VMD-PF-ARIMA & **0.2367** & - \\  & [38] & 84 & Capacity & SVMD-AO-DELM & 1.33 & 0.9912 \\ \hline \multirow{8}{*}{**NASA(B007)**} & Proposed & 100 & Capacity & GA-CNN & **0.1895** & **0.9987** \\  & [29] & 100 & SOH & CL-GraphSAGE & 0.5314 & 0.950810 \\ \cline{1-1}  & [30] & 100 & RUL & GPR-GMR & 0.90 & - \\ \cline{1-1}  & [31] & \%60 & Residual life & CNN-LSTM & 0.52221 & - \\ \cline{1-1}  & [32] & 100 & Capacity & VMD-PF-ARIMA & 0.6267 & - \\ \cline{1-1}  & [38] & 84 & Capacity & SVMD-AO-DELM & 0.79 & 0.9942 \\ \hline \multirow{8}{*}{**NASA(B0018)**} & Proposed & 70 & Capacity & GA-CNN & **0.5958** & **0.9959** \\  & [33] & 76 & SOH & CNN-BiLSTM-AM & 1.09 & - \\ \cline{1-1}  & Proposed & 100 & Capacity & GA-CNN & **0.3670** & **0.9909** \\ \cline{1-1}  & [32] & 100 & Capacity & VMD-PF-ARIMA & 0.6575 & - \\ \cline{1-1}  & [38] & 84 & Capacity & SVMD-AO-DELM & 1.64 & 0.9753 \\ \hline \hline \end{tabular}  
* The best results are indicated in **bold**.  
\end{table} 
Table 10: Comparison of the literature. **Funding:** This research was funded by the scientific research projects coordination unit of ondokuz mayis university, grant number PYO.MUH.1904.23.004.