# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 5 Result and Discussion
### SOC Estimation of MNN and LSTM
#### 5.2.2 Comparison of Use/Unused of Temperature Variable Data
Cell 1 to Cell 4 are also shown in the Table 6.  
As a result of the experiment, it was confirmed that the SOC of a lithium battery using the LSTM method took a little longer to learn, but showed higher accuracy than when the SOC was estimated using the MNN model, and a low error rate was recorded. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MNN/LSTM** & **Cell 1** & **Cell 2** & **Cell 3** & **Cell 4** \\ \hline MNN (Room temperature) & 3.6\% & 3.4\% & 4.9\% & 1.8\% \\ LSTM (Room temperature) & 2.7\% & 3.3\% & 3.9\% & 0.8\% \\ MNN (High temperature) & 2.8\% & 0.8\% & 1.4\% & 1.8\% \\ LSTM (High temperature) & 1.3\% & 0.7\% & 1.5\% & 0.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: SOC estimation results of using the MNN and LSTM at room temperature and high temperature. Figure 12: SOC estimation results with temperature data at high temperature (**a**) Using MNN (**b**) Using LSTM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature Variable Use/Unused** & **Cell 1** & **Cell 2** & **Cell 3** & **Cell 4** \\ \hline Unused (Room temperature) & 2.9\% & 6.1\% & 3.9\% & 3.1\% \\ Use (Room temperature) & 2.7\% & 3.3\% & 3.9\% & 1.8\% \\ Unused (High temperature) & 1.9\% & 2.6\% & 3.5\% & 1.3\% \\ Use (High temperature) & 1.3\% & 0.7\% & 1.5\% & 0.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the SOC estimation results using the LSTM of Cell 1 to Cell 4 according to the temperature variable. In addition, the result of the experiment revealed that the error rate tends to be less when 6 V is used as the input value at both room temperature and high temperature. This confirms the tendency of the error rates to be lower than when using 2 and 8 voltages as the input values. It has also proven that the error rate is generally lower when using the LSTM than when using the MNN. For real-time SOC estimation, 6 voltage and temperature variable data were utilized as input layers, and the SOC of each cell of the lithium battery pack was estimated in real time using the LSTM. According to [PERSON] et al. (2013), the SOC estimation results of lithium battery, using the cubature Kalman filter method, showed a performance of error within 5%. In our proposed method, the average error was within 3% at room temperature and within 1% at a high temperature (40 \({}^{\circ}\)C). From these results, the performance of our proposed method was better than that of the above paper (2013). According to [PERSON], et al. (2011), the SOC of a lithium battery was estimated using the LSTM model and obtained the SOC estimation error of 1.68% at room temperature and an average of 1.26% at a high temperature (40 \({}^{\circ}\)C). It was confirmed that the performance of our proposed method was worse than [PERSON]'s paper at room temperature. However, the performance of our proposed method at high temperatures was better than that proposed in [PERSON]'s paper. In addition, the two papers (2013; 2013) were applied to estimate the SOC using only the battery cell. Conversely, the proposed method estimated the SOC of the battery for the battery pack that comprised four cells and the observed SOC estimation performance was slightly lowered.