Adaptive capacity trajectory prediction for lithium-ion batteries using electrochemical impedance spectroscopy

Lithium-ion batteries (LIBs) are extensively deployed in industrial fields. To ensure the reliability and efficiency of LIBs, it is crucial to conduct capacity trajectory prediction. However, existing model-based and deep learning (DL) approaches for LIBs capacity trajectory prediction are constrained by imprecise modeling, insufficient analysis of internal states, and the over-reliance on single-source domain transfer learning (TL). To accurately predict LIBs capacity trajectory under varying environmental conditions, a novel electrochemical impedance spectroscopy-based multi-source domain TL framework is proposed in this study. The distribution of relaxation times technique and equivalent circuit model are employed to extract timescale and physical parameters for internal states investigation, and key features are selected as inputs for the DL models, which produce capacity estimation and the cycle intervals between knots corresponding to specific capacity levels. The capacity trajectory is subsequently predicted by applying the piecewise cubic Hermite interpolating polynomial to these knots. Besides, the two-stage weight allocation strategy is developed for the effective integration of multiple source domains, where the first stage involves weight allocation to LIBs within each source domain and the second stage focuses on assigning domain-specific weights. Experiments on two publicly available EIS datasets validate the effectiveness and generalization capability of the proposed framework across different temperatures and LIBs types. Comparison results with state-of-the-art TL methods further demonstrate the necessity of the two-stage weight allocation strategy and highlight the framework superiority in accurately and reliably predicting capacity trajectories.