# Application of Deep Learning Techniques for the State of Charge Prediction of Lithium-Ion Batteries
###### Abstract  
This study proposes a deep learning-based long short-term memory (LSTM) model to predict the state of charge (SOC) of lithium-ion batteries. The purpose of the research is to accurately model the complex nonlinear behavior that occurs during the charging and discharging processes of batteries to predict the SOC. The LSTM model was trained using battery data collected under various temperature and load conditions. To evaluate the performance of the artificial intelligence model, measurement data from the CS2 lithium-ion battery provided by the University of Maryland College of Engineering was utilized. The LSTM model excels in learning long-term dependencies from sequence data, effectively modeling temporal patterns in battery data. The study trained the LSTM model based on battery data collected from various charge and discharge cycles and evaluated the model's performance by epoch to determine the optimal configuration. The proposed model demonstrated high SOC estimation accuracy for various charging and discharging profiles. As training progressed, the model's predictive performance improved, with the predicted SOC moving from 14.8400% at epoch 10 to 12.4968% at epoch 60, approaching the actual SOC value of 13.5441%. Simultaneously, the mean absolute error (MAE) and root mean squared error (RMSE) decreased from 0.9185% and 1.3009% at epoch 10 to 0.2333% and 0.5682% at epoch 60, respectively, indicating continuous improvement in predictive performance. In conclusion, this study demonstrates the effectiveness of the LSTM model for predicting the SOC of lithium-ion batteries and its potential to enhance the performance of battery management systems. long short-term memory; lithium ion battery; state of charge estimation; MAE; RMSE +
Footnote †: journal: Applied  
extended Kalman filter (EKF), enable nonlinear system estimation for linear time-varying systems, but they have disadvantages such as implementation complexity and the need to consider many parameters during modeling [8; 9; 10]. To address these challenges, various technologies, including artificial intelligence algorithms, have been proposed, and recent advances in artificial intelligence and computer equipment, coupled with the explosive increase in data, have heightened interest in AI [11]. AI-based methods for SOC estimation include artificial neural networks (ANN), support vector machines (SVM), fuzzy algorithms, and recurrent neural networks (RNN), which provide high accuracy across various current ranges [12; 13]. However, since the SOC of a battery is calculated as the accumulated amount of current, it is necessary to include sequence information in the estimation network. RNNs can learn data that change over sequences through state variables, and with the introduction of memory cells such as LSTM, it has become possible to learn large-scale data over long periods. Recently, studies have also been conducted to estimate SOC in combination with the convolutional neural network (CNN) method [14; 15]. Recently, various AI models have been applied in the field of SOC estimation for lithium-ion batteries, and performance comparisons and improvements of different approaches have been actively conducted. For instance, traditional filter-based models like the Kalman filter (KF) and extended Kalman filter (EKF) have been widely used for SOC estimation; however, these models face limitations in handling nonlinear data and complexity in implementation. Additionally, studies have been conducted to evaluate the accuracy and efficiency of SOC estimation using various machine learning algorithms, providing a broad understanding of SOC estimation model performance optimization, but often lacking a focus on the detailed performance enhancement of specific models [16]. On the other hand, other AI-based models, such as support vector machines (SVM), have shown excellent performance in handling nonlinear characteristics but are limited in their ability to learn from sequence data [17].