Gezielte Temperierung in der Warmmassivumformung

The State of Health (SOH), a critical metric for evaluating battery aging and performance degra- dation, requires accurate estimation to ensure the safe operation and lifespan management of battery systems. Com-pared to the well-established lithium-ion battery systems, the aging mechanism and capacity degradation behavior of sodium-ion batteries remains insufficiently understood. In this study, a SOH estimation method for sodium-ion bat-teries is proposed by fusing incremental capacity (IC) and relaxation voltage (RV) features. The IC curves are em-ployed to analyze phase transition dynamics during charge/discharge processes, while RV features are utilized to characterize electrode polarization recovery patterns during resting periods, thereby comprehensively revealing multi-dimensional aging mechanism. A feature fusion model is developed to enhance the sensitivity and noise immu-nity of health indicators. By leveraging machine learning algorithms, the mapping relationship between IC/RV-de-rived features and SOH is established, constructing an LSTM-Attention (Long Short-Term Memory network integrat-ed with an attention mechanism) based estimation model. The experimental results show that the proposed method achieves superior SOH estimation accuracy (RMSE<0.51%, MAE<0.40%) compared to single-feature approaches, providing a robust solution for real-time health monitoring and industrial deployment of sodium-ion batteries.