# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 2 Background
### Traditional SOC Estimation Method  
Several methods have been proposed for SOC estimation using the data from the current battery. There are battery SOC estimation methods, such as Kalman Filter method [13], Open-Circuit Voltage (OCV) method [14], and neural network method [15]. When charging and discharging the lithium battery, the data shows nonlinear characteristics. In Kalman filtering, SOC estimation can be performed in real time because the extended Kalman filter method is often used because of the nonlinear characteristics of the battery. However, the calculation time is increased according to the increase in the variable [16]. In the OCV method, SOC estimation is performed based on the fact that the remaining capacity of the battery naturally decreases in proportion to its energy usage. However, real-time SOC estimation, using this method is difficult, as it requires a large amount of time, requires the battery to be in an equilibrium state, and requires the measurement of the terminal voltage [17]. The MNN and RNN are used to obtain data exhibiting non-linear characteristics of lithium batteries. When using MNN, several hidden layers need to be added in the existing neural network composed of faults to obtain data. Through it, the relationship between complex inputs and outputs can be determined. The MNN also has the advantage of adapting well to data, which is suitable for the control problems of nonlinear systems [18; 19]. Conversely, the RNN sends the result from the node of the hidden layer to that of the output layer and then sends it back to the calculation input of the node of the other hidden layer. In general, the RNN is more efficient than the MNN and can be used to achieve better accuracy [20]. We compared the advantages and disadvantages of the conventional OCV method, Kalman filter method, neural network method, which are frequently used for SOC estimation, and the proposed method in Table 1[16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SOC Estimation Method** & \multicolumn{2}{c}{**Advantage**} & \multicolumn{2}{c}{**Disadvantage**} \\ \hline \multirow{4}{*}{ \begin{tabular}{c} OCV \\ [17; 22; 23; 24] \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} It is suitable for estimating \\ the initial SOC. \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} • It is difficult to use in general \\ because it requires accurate \\ voltage measurement. \\ \end{tabular}  } \\  & & & The algorithm is simple \\ \cline{1-1}  & & to implement. & \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Kalman filter \\ [16; 25; 26; 27] \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} It is useful for learning the \\ nonlinear characteristics of \\ a battery. \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} • It is difficult to apply because the \\ parameters and algorithms \\ are complex. \\ \end{tabular}  } \\  & & & It has great error \\ \cline{1-1}  & & & correction capability. \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Conventional Neural Network \\ based method [18; 19; 20; 21] \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} It is useful for learning the \\ nonlinear characteristics of \\ a battery. \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} It only estimates SOC for each \\ battery cell. \\ There has not been much research \\ on its use in the SOC estimation for \\ battery packs. \\ \end{tabular}  } \\  & & & It can learn complex \\ \cline{1-1}  & & input/output relationships. & \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Proposed method \\ \end{tabular}  } & \multirow{4}{*}{\ \begin{tabular}{c} It is possible not only to \\ estimate the SOC of the \\ existing battery cell but also \\ that of the battery pack. \\ It is possible to estimate SOC \\ in real time. \\ \end{tabular}  } & \multirow{4}{*}{ \begin{tabular}{c} In the case of the LSTM, \\ the learning time is relatively \\ increased compared with MNN. \\ \end{tabular}