State-of-charge estimation of sodium-ion batteries: A fusion deep learning approach

Sodium-ion batteries (SIBs) have shown great promise as an alternative to lithium-ion batteries (LIBs) due to abundant sodium resources. Accurate and universal state of charge (SOC) estimation is essential for building an effective battery management system (BMS) for these emerging batteries. However, traditional SOC estimation methods for LIBs cannot be directly applied to SIBs due to significant differences in charge–discharge mechanisms and electrochemical characteristics. To address challenges with SIBs, this study proposes a novel framework integrating deep learning models. The BiLSTM is implemented to learn patterns from current and voltage time series. Additionally, the N-BEATS network extracts high-level features without manual feature engineering to mitigate the low sensitivity of SOC to voltage. By combining strengths of both networks, the fused model enhances SOC prediction robustness. Specifically, the proposed model is trained under various operating conditions and evaluated on both training and untrained datasets. Experiments demonstrate the fused model reduces root mean square error (RMSE) by 11.24% and 74.44% compared to individual N-BEATS and BiLSTM networks. The SOC estimation achieves mean absolute error (MAE) and RMSE below 0.30% and 0.39%, respectively. This research can inform the development of effective BMS for practical applications of SIBs, paving the way to the application of the new battery type.