# Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Train Num.** & **Algorithm** & **MSE (\%)** & **RMSE (\%)** & **NRMSE (\%)** & **MAE (\%)** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{**40**} & GA-CNN & **0.3505** & **5.9205** & **14.2577** & **5.3443** & **0.9996** \\  & GA-BP & 4.3393 & 20.8311 & 50.1655 & 18.5313 & 0.5727 \\  & GA-RNN & 4.1778 & 20.4396 & 49.2228 & 18.1883 & 0.9102 \\ \hline \multirow{4}{*}{**70**} & GA-CNN & **0.0019** & **0.4320** & **1.4982** & **0.3869** & **0.9993** \\  & GA-BP & 0.0195 & 1.3976 & 4.8468 & 0.9576 & 0.9892 \\  & GA-RNN & 0.4683 & 6.8432 & 23.7311 & 5.8259 & 0.8097 \\ \hline \multirow{4}{*}{**100**} & GA-CNN & **0.0004** & **0.1895** & **1.0859** & **0.1553** & **0.9987** \\  & GA-BP & 0.0103 & 1.0156 & 5.8193 & 0.8077 & 0.9970 \\ \cline{1-1}  & GA-RNN & 0.0370 & 1.9231 & 11.0191 & 0.9035 & 0.9025 \\ \hline \hline \end{tabular} The best results are indicated in **bold**.  
\end{table} 
Table 7: Battery B0007 performance parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Train Num.** & **Algorithm** & **MSE (\%)** & **RMSE (\%)** & **NRMSE (\%)** & **MAE (\%)** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{**40**} & GA-CNN & **0.0031** & **0.5582** & **1.4475** & **0.4225** & **0.9983** \\  & GA-BP & 1.4980 & 12.2395 & 31.7368 & 10.5903 & 0.5905 \\  & GA-RNN & 2.3731 & 15.4049 & 39.9446 & 13.0404 & 0.1834 \\ \hline \multirow{4}{*}{**70**} & GA-CNN & **0.0035** & **0.5958** & **3.0970** & **0.5074** & **0.9959** \\  & GA-BP & 0.1404 & 3.7469 & 19.4772 & 3.2231 & 0.9706 \\  & GA-RNN & 0.2685 & 5.1822 & 28.3498 & 4.5856 & 0.6247 \\ \hline \multirow{4}{*}{**100**} & GA-CNN & **0.0013** & **0.3670** & **3.0739** & **0.3177** & **0.9909** \\  & GA-BP & 0.0016 & 0.3956 & 3.3136 & 0.3306 & 0.9835 \\ \cline{1-1}  & GA-RNN & 0.0382 & 1.9547 & 16.3739 & 1.0115 & 0.7096 \\ \hline \hline \end{tabular} The best results are indicated in **bold**.  
\end{table} 
Table 8: Battery B0018 performance parameters. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Battery** & **Train Num.** & **Algorithm** & **RMSE (\%)** & **R\({}^{2}\)** \\ \hline \multirow{4}{*}{B0005} & 40 & GA-CNN & 5.2588 & 0.9999 \\  & 70 & GA-CNN & 0.2794 & 0.9999 \\  & 100 & GA-CNN & 0.1176 & 0.9996 \\ \hline \multirow{4}{*}{B0006} & 40 & GA-CNN & 3.9527 & 0.9505 \\  & 70 & GA-RNN & 6.8336 & 0.7026 \\  & 100 & GA-CNN & 4.4784 & 0.7991 \\ \hline \multirow{4}{*}{B0007} & 40 & GA-CNN & 5.9205 & 0.9996 \\  & 70 & GA-CNN & 0.4320 & 0.9993 \\ \cline{1-1}  & 100 & GA-CNN & 0.1895 & 0.9987 \\ \hline \multirow{4}{*}{B0018} & 40 & GA-CNN & 0.5582 & 0.9983 \\ \cline{1-1}  & 70 & GA-CNN & 0.5958 & 0.9959 \\ \cline{1-1}  & 100 & GA-CNN & 0.3670 & 0.9909 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Best prediction results. 100 training data for the B0006 battery. Specifically, predictions with 100 training data for battery B0005 achieve an RMSE performance that is twice as good as in the literature studies. Similarly, the results obtained using 100 training data for battery B0007 are approximately three times better than the literature studies, while predictions with 70 and 100 training data for battery B0018 are about twice as good as the literature studies. Notably, the prediction with 70 training data for battery B0007 is achieved with the proposed model, which outperforms existing studies in the literature. Figure 11: NASA batteries capacity–cycle predictions for **(a)** B0005, 40 train data; **(b)** B0005, 70 train data; **(c)** B0005, 100 train data; **(d)** B0006, 40 train data; **(e)** B0006, 70 train data; **(f)** B0006, 100 train data; **(g)** B0007, 40 train data; **(h)** B0007, 70 train data; **(i)** B0007, 100 train data; **(j)** B0018, 40 train data; **(k)** B0018, 70 train data; and **(l)** B0018, 100 train data.