Deep Learning Based SOC Estimation Method for Battery in Energy Storage Field Station

Accurate State of Charge (SOC) estimation is essential for battery management in energy storage field stations, where real-time and precise monitoring is crucial for operational efficiency and safety. Traditional SOC estimation methods, such as Coulomb counting and model-based approaches, face challenges with accuracy and adaptability under dynamic conditions. In this study, we explore deep learning-based SOC estimation methods by experimenting with three prominent architectures: Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN).Each model was trained using historical battery data, including voltage, current, and temperature, to evaluate its ability to predict SOC. The RNN model demonstrates the capability to capture temporal dependencies in battery data but suffers from vanishing gradient issues when dealing with long sequences, leading to lower accuracy. The LSTM model, designed to overcome these limitations, provides improved performance by learning long-term dependencies more effectively, yielding higher accuracy and stability in SOC estimation under fluctuating conditions. CNN, on the other hand, excels in feature extraction by identifying spatial patterns in multidimensional data, offering faster computation and robust results but lacking the ability to capture sequential dependencies as efficiently as RNN-based methods.Comparative analysis reveals that LSTM offers the best balance between accuracy and generalization, particularly for long-term and dynamic operational conditions, while CNN provides the fastest and computationally efficient solution with reasonable accuracy. RNN, though useful for simple tasks, is less suitable for complex SOC estimation. This research highlights the strengths and limitations of each approach, guiding the development of more effective SOC estimation systems for large-scale energy storage management.