Accurate prediction of the remaining useful life (RUL) of lithium batteries is essential for ensuring efficient equipment maintenance and energy management, particularly as these batteries serve as a core driver in the new energy technology revolution. While deep learning models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and their variants have demonstrated significant success in RUL prediction, they often face challenges related to inadequate modelling of long-term dependencies in complex degradation data. To overcome these limitations, this paper proposes a novel hybrid architecture that integrates the Kolmogorov-Arnold Network (KAN) with an Extended Long Short-Term Memory Network (xLSTM). The KAN component enhances high-dimensional function approximation and improves parameter efficiency by substituting traditional linear weights with B-spline-parameterized univariate functions. Meanwhile, the xLSTM introduces exponential gating mechanisms and covariance update rules to more effectively capture high-order long-term dependencies. Experimental results on the NASA lithium battery aging dataset demonstrate that the proposed KAN-xLSTM model significantly outperforms CNN, LSTM, and xLSTM models in prediction accuracy, particularly for battery groups with large capacity fluctuations.
Hu et al. (Sun,) studied this question.