Abstract
A framework integrating deep learning and nonlinear Wiener process (NWP) is proposed for the remaining useful life prediction of lithium-ion batteries. First, degradation features from multi-source sensor data are automatically extracted using an autoencoder incorporating embedded L1 regularization. Second, a one-dimensional composite performance indicator is constructed via neural networks to accurately describe the performance degradation trajectory of the battery. Subsequently, a stochastic degradation model based on the NWP is established, wherein complex degradation dynamics are effectively captured by a time-varying drift function. On this basis, a Bayesian posterior update mechanism is integrated to adaptively adjust model parameters online, whereby individual differences are quantified. Finally, experimental verification on public datasets demonstrates that the proposed method reduces root mean square error and mean absolute error by more than 20%, and significant improvements in prediction accuracy are achieved via the data-model interaction architecture that combines feature compression, degradation quantification, and parameter adaptation.