# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
## 4 Implementation and Results
### Results of Applying the LSTM Model for SOC Inference  
In this paper, model parameters are parameters necessary for model construction or training process and include layers, units, batch sizes, forks, etc. In this chapter, we will compare and analyze the actual SOC and predicted SOC according to epochs 10, 20, 30, 40, 50, and 60 to estimate the appropriate SOC value from the dynamic profile. All model parameters except the parameters selected here are set the same. Looking at Table 4 and Figure 8 together, we see that the model tends to predict closer to the actual SOC value as the epoch progresses, but tends to slightly underestimate or overestimate the SOC value in certain epochs. Epoch 60 shows the most accurate predictions, suggesting that the model is gradually optimizing with sufficient training. However, the predicted values remained very close to the actual values in all epochs, indicating that the model's performance was stable. This study proposes a deep learning-based approach using an LSTM model to predict the state of charge (SOC) of lithium-ion batteries. The LSTM model is well-suited for modeling the complex, nonlinear patterns that occur during the charging and discharging processes of batteries due to its ability to learn long-term dependencies from sequential data. The data used in this experiment was obtained from the University of Maryland, consisting of measurements from CS2 lithium-ion batteries collected under various temperature and load conditions. The input variables for training the LSTM model included voltage (\(V_{t}\)) and current (\(I_{I}\)) at each time step. \begin{table}
\begin{tabular}{c c c} \hline
**Epoch** & **Actual SOC (\%)** & **Predicted SOC (\%)** \\ \hline Epoch 10 & 13.5441 & 14.8400 \\ Epoch 20 & 13.5441 & 12.6617 \\ Epoch 30 & 13.5441 & 12.4947 \\ Epoch 40 & 13.5441 & 12.6203 \\ Epoch 50 & 13.5441 & 12.5185 \\ Epoch 60 & 13.5441 & 12.4968 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of actual and predicted SOC values according to exposition. The LSTM network was designed to predict SOC at each time step based on the input data: voltage, current, and temperature. The LSTM cell, which consists of input gates, forget gates, and output gates, decides whether to retain or discard information, thus enhancing the model's learning capability. The model was trained using data from various charge and discharge cycles, and the performance was evaluated at each epoch (training iteration) to determine the optimal configuration settings. Table 5 and Figure 9 present the predicted SOC, MAE, and RMSE values for each epoch, allowing us to analyze the model's training process and performance. In the early stages, such as epoch 10, the predicted SOC was 14.8400%, with MAE at 0.9185 and RMSE at 1.3009. These higher error values indicate that the model was not yet optimized. However, as the training progressed to epoch 20, the predicted SOC decreased to 12.6617%, MAE reduced to 0.5693, and RMSE to 0.8812, demonstrating that the model was learning and improving. By epoch 30, the predicted SOC was 12.4947%, with further reductions in MAE and RMSE to 0.3403 and 0.6511, respectively. This trend of increasing accuracy and decreasing error continued, with epoch 40 showing a predicted SOC of 12.6203, MAE of 0.2706, and RMSE of 0.5968. The continuous decline in errors indicates that the model was becoming increasingly precise. By epoch 50, the predicted SOC was 12.5185%, with MAE  
Figure 8: Actual and predicted battery SOC values for (**a**) epoch 10, (**b**) epoch 20, (**c**) epoch 30, (**d**) epoch 40, (**e**) epoch 50, and (**f**) epoch 60. A graph showing the difference between values. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Epoch** & **Predicted SOC** & **MAE** & **RMSE** \\ \hline Epoch 10 & 14.8400 & 0.9185 & 1.3009 \\ Epoch 20 & 12.6617 & 0.5693 & 0.8812 \\ Epoch 30 & 12.4947 & 0.3403 & 0.6511 \\ Epoch 40 & 12.6203 & 0.2706 & 0.5968 \\ Epoch 50 & 12.5185 & 0.2331 & 0.5793 \\ Epoch 60 & 12.4968 & 0.2333 & 0.5682 \\ \hline \hline \end{tabular}
\end{table}