# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
## 5 Conclusions  
Accurate condition prediction (SOC) of lithium-ion batteries plays an important role in various applications such as electric vehicles and smart grids. Accurate estimation of SOC is essential to ensure efficient use, longevity, and safety of batteries. However, accurately estimating SOC is a challenge due to the complex nonlinear nature of batteries and the varying operating environments. In this study, we propose a method to estimate the SOC of lithium-ion batteries using deep learning, especially short- and LSTM models. LSTM has a great ability to learn long-term dependencies from sequence data and can effectively model temporal patterns in battery data. This study used battery data collected from different charge and discharge cycles to train an LSTM model and evaluated the model's performance by epoch to derive the optimal model configuration. Through this method, this study aimed to improve the accuracy and reliability of SOC estimation of lithium-ion batteries. In this paper, we propose a method for estimating the SOC (condition prediction) of lithium-ion batteries using the LSTM model, and explore the possibility of improving the performance of the battery management system through it. The proposed model has shown highly accurate SOC estimations for a variety of charge and discharge profiles, especially when concluding based on the data provided that the predicted SOC value tends to be closer and closer to the actual SOC value of 13.5441% as the epoch progresses. In epoch 10, the predicted SOC was 14.8400%, which was significantly higher than the actual value, but as we progressed to epoch 60, the predicted SOC was 12.4968%, which was closer to the actual value. This means that as training progresses, the accuracy of the model was improving. At the same time, the MAE and root mean squared error (RMSE) were also decreasing. In epoch 10, MAE was 0.9185 and RMSE was 1.3009, but in epoch 60 MAE was 0.2333 and RMSE was 0.5682. This decreasing trend indicates that the model's predictive performance was improving.  
Overall, as the model learned, it approached the actual SOC value more accurately, and the error rate was constantly decreasing. Therefore, this model can be evaluated as a useful tool for SOC prediction.  
Figure 9: Comparison of (**a**) MAE and (**b**) RMAE across epochs.  
**Author Contributions:** S.-B.K. and S.-H.L.: writing--original draft, data curation, software, and visualization. S.-B.K.: writing--review and editing. S.-H.L.: conceptualization, validation, writing--review and editing, and project administration. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-002).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data, models, and codes generated or used during the study are available from the corresponding author upon request.  
**Conflicts of Interest:** The authors declare no conflict of interest.