Frequency-Weighted EIS Manifold Learning for Lithium-Ion Battery Remaining Useful Life Prediction

Accurate remaining useful life (RUL) prediction of lithium-ion batteries is essential for ensuring the reliability and safety of battery management systems. However, conventional electrical and thermal signals are sensitive to operating conditions, while linear feature extraction methods may not adequately characterize the nonlinear electrochemical aging information embedded in electrochemical impedance spectroscopy (EIS). Moreover, the unequal degradation relevance of different impedance-frequency regions is rarely considered. To address these limitations, this paper proposes a frequency-weighted EIS manifold-learning framework for battery RUL prediction. A band-level frequency-weighting strategy is first introduced to incorporate degradation-related frequency priors, after which kernel principal component analysis (KPCA) is employed to construct compact nonlinear representations of EIS evolution, followed by support vector regression (SVR) with hyperparameter optimization. Linear bias correction and moving-average smoothing are further incorporated to improve prediction consistency. Experiments on seven batteries within the investigated LR2032 dataset, including one chronological test and six held-out-cell tests, demonstrate that the proposed framework consistently outperforms the baseline models, achieving an average MSE of 59.434 cycles2 and an average R2 of 0.989.