# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
## 4 Implementation and Results
### DataSet  
The experiments on the CS2 lithium-ion batteries used in this paper were conducted in the environment shown in Table 1. All CS2 cells underwent the same charging profile, a standard constant current/constant voltage protocol with a constant current rate of 0.5 C until the voltage reached 4.2 V, followed by 4.2 V until the charge current dropped below 0.05 A. Unless specified, the discharge cutoff voltage for these batteries was 2.7 V. All CS2 cells were randomly numbered and named accordingly. The nth CS2 cell is given the name 'CS2_n'[23]. Table 1 utilizes the dataset from the measurements of CS2 lithium-ion batteries provided by the College of Engineering at the University of Maryland to evaluate the AI model. The Excel data measured for the CS_2 lithium-ion batteries were composed of four cells: CS35, CS36, CS37, and CS38, as shown in Figure 6, and these were consolidated into a single dataset. Additionally, columns for the voltage generated using calculated values for each cell [3.7, 3.8, 3.9, 4.0, 4.1, 4.2], SOC, OCV, ID, Date_Time, Cycle_Index, Charge_Capacity (Ah), and Discharge_Capacity (Ah) were added. Table 2 below shows four data item numbers and added columns: CS_35, CS_36, CS_37, and CS_38. This table shows the change in actual state of charge (SOC) values and Validation Loss over each epoch during the training of the LSTM model. An epoch means that the entire training dataset was fed to the model once, and this table represents data from epochs 10 through 60. In particular, the model seems to be performing best at Epoch 60, with the lowest validation loss (0.3192%). Overall, both the true SOC value and the verification loss tend to decrease as the epoch increases, showing that the model's prediction performance is gradually improving with training. Figure 7 shows the training loss and validation loss according to the epochs for the generalized dataset. Both losses declined rapidly initially, and then remained at a stable low level. \begin{table}
\begin{tabular}{c c} \hline \hline
**Battery (Parameters)** & **Specifications (Value)** \\ \hline Capacity Rating & 1100 mAh \\ Cell Chemistry & LiCoO2 cathode, EDS results also showed trace \\ elements of Manganese \\ Weight (w/o safety circuit) & 21.1 g \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of CS2 lithium-ion batteries. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Decision** & **Battery Type** & **Number of Data** & **Training Data** & **Test Data** \\ \hline \multirow{4}{*}{Battery Dataset} & CS35 & 911 & \\  & CS36 & 950 & \\  & CS37 & 1016 & \\  & CS38 & 1056 & \\ \hline Total & 4 Cell & 3933 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types of battery data. Figure 6: Format of the dataset. Table 3 and Figure 7 show the training loss and validation loss of epoch 10, 20, 30, 40, 50, and 60 in the training process of the LSTM model. Loss reduction, both training and validation losses, tend to decrease consistently as the epoch progresses. This means that the model is learning from your data, and its performance is gradually improving. There was no significant difference between the loss of training and the loss of validation. This indicates that overfitting was fairly well managed. If there is overfitting, the training loss will continue to decrease, but the verification loss will tend to increase. Performance gains went from epoch 10 to 60, while training loss decreased from 2.5654 to 1.1306 and validation loss decreased from 1.5072 to 0.3192. This suggests that the model was increasingly making accurate predictions. Optimal model selection had the lowest validation loss at epoch 60, so it can be assumed that the model had the best predictive performance at this point. However, it is necessary to consider the point at which the decline in losses will moderate, and decide at what point to proceed further with the learning or stop early. In conclusion, the performance of the model was improving as it learned, and it seemed that it was learning stably without overfitting.