With the widespread application of energy storage stations, BMS has become an important subsystem in modern power systems, leading to an increasing demand for improving the accuracy of SOC prediction in lithium-ion battery energy storage systems. Currently, common methods for predicting battery SOC include the Ampere-hour integration method, open circuit voltage method, and model-based prediction techniques. However, these methods often have limitations such as single-variable research, complex model construction, and inability to capture real-time changes in SOC. In this paper, a novel prediction method based on the KF-SA-Transformer model is proposed by combining model-based prediction techniques with data-driven methods. By using temperature, voltage, and current as inputs, the limitations of single-variable studies in the Ampere-hour integration method and open circuit voltage method are overcome. The Transformer model can overcome the complex modeling process in model-based prediction techniques by implementing a non-linear mapping between inputs and SOC. The presence of the Kalman filter can eliminate noise and improve data accuracy. Additionally, a sparse autoencoder mechanism is integrated to optimize the position encoding embedding of input vectors, further improving the prediction process. To verify the effectiveness of the algorithm in predicting battery SOC, an open-source lithium-ion battery dataset was used as a case study in this paper. The results show that the proposed KF-SA-Transformer model has superiority in improving the accuracy and reliability of battery SOC prediction, playing an important role in the stability of the grid and efficient energy allocation.
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