# 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  
In this paper, an experiment was conducted to estimate the SOC of each cell for the lithium battery packs in the room temperature range of 25-26 \({}^{\circ}\)C and high temperature range of 39-42 \({}^{\circ}\)C. The input value using only the voltage data of room temperature and high temperature without the temperature variable data and the input value of room temperature, and high temperature at which the temperature variable data was added were compared. Figure 8 shows a graph of the SOC estimation result when using the MNN of Cell 4 utilizing only 6 voltage as the input value at room temperature and high temperature. Figure 9 also shows a graph of the SOC estimation result when using the LSTM of Cell 4 utilizing only 6 voltage as the input value at both room temperature and high temperature. \begin{table}
\begin{tabular}{c c c c c} \hline
**Number of Inputs** & **Cell 1** & **Cell 2** & **Cell 3** & **Cell 4** \\ \hline
2 (Room temperature) & 7.2\% & 7.5\% & 4.4\% & 2.2\% \\
2 (Hight temperature) & 1.4\% & 2.9\% & 1.4\% & 1.5\% \\
6 (Room temperature) & 4.8\% & 6.9\% & 3.8\% & 2.2\% \\
6 (Hight temperature) & 3.1\% & 3.4\% & 3.1\% & 1.3\% \\
8 (Room temperature) & 5.0\% & 7.2\% & 4.2\% & 2.8\% \\
8 (Hight temperature) & 2.8\% & 3.1\% & 3.4\% & 1.1\% \\ \hline \end{tabular}
\end{table} 
Table 2: Results of SOC estimation using the MNN according to the difference in the number of inputs. \begin{table}
\begin{tabular}{c c c c c} \hline
**Number of Inputs** & **Cell 1** & **Cell 2** & **Cell 3** & **Cell 4** \\ \hline
2 (Room temperature) & 3.4\% & 9.2\% & 7.3\% & 4.8\% \\
2 (Hight temperature) & 4.9\% & 1.6\% & 1.3\% & 2.0\% \\
6 (Room temperature) & 2.9\% & 6.1\% & 3.9\% & 3.1\% \\
6 (Hight temperature) & 2.7\% & 2.6\% & 3.5\% & 1.8\% \\
8 (Room temperature) & 1.2\% & 1.8\% & 4.9\% & 4.2\% \\
8 (Hight temperature) & 1.9\% & 1.4\% & 4.2\% & 1.9\% \\ \hline \end{tabular}
\end{table} 
Table 3: Results of SOC estimation using the LSTM according to the difference in the number of inputs. The \"origin\" label in Figures 8-11 shows the real percentage SOC values and these can be calculated as follows,  
\[\text{SOC}=\frac{C_{\textit{releasable}}}{C_{\textit{rated}}}\times 100 \tag{15}\]  
where, \(C_{\textit{releasable}}\) is the initial capacity of the battery, and \(C_{\textit{rated}}\) is the capacity of the battery currently available. In this paper, the above equation is used to calculate the time for SOC estimation and the SOC origin of the lithium battery pack. Figure 8: SOC estimation results (**a**) Using the MNN utilizing only 6 voltage as the input value at room temperature (**b**) Using the MNN utilizing only 6 voltage as the input value at high temperature. Figure 9: SOC estimation results (**a**) Using the LSTM utilizing only 6 voltage as the input value at room temperature (**b**) Using the LSTM utilizing only 6 voltage as the input value at high temperature. As a result of the experiment, compared to using 6 voltage data as input values, if 6 voltage data and 1 temperature variable data are used as input values, with the MNN model, the error rate decreases. With the LSTM method, when voltage and temperature variable data are used together as with the MNN model, the error rate tends to decrease also. 2.3 Comparison of SOC Estimation Performance of Multi-Layer Neural Network and Long Short-Term Memory  
In this paper, the MNN and LSTM were used to estimate the SOC of lithium batteries. Figure 12 shows a graph of the results using the MNN and LSTM of Cell 4 utilizing 6 voltage as the input value and the temperature variable data at high temperature. Table 6 compares the results of using the MNN and LSTM utilizing only 6 voltage as the input value and the temperature variable data at both room temperature and high temperature.