Remaining Useful Life Prediction for Lithium-Ion Batteries Based on a Deep Mixed-Effect Gaussian Process Model

Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries is essential for prognostics and health management. However, standard Gaussian processes (GPs) face challenges in scalability and capturing complex global degradation trends, while deep learning models often lack principled uncertainty quantification. To bridge this gap, this study proposes a novel deep mixed-effect Gaussian process (DME-GP) model, which decomposes the predictive function into a global multi-layer perceptron (MLP)-based feature mapping component and a sample-specific local GP component under the mixed-effect paradigm. This hybrid architecture synergistically captures intricate global patterns and provides probabilistic uncertainty estimates. The model’s performance was rigorously validated on a real-world battery RUL dataset. Quantitative results demonstrate its superior accuracy, achieving a reduction in root mean square error (RMSE) by up to 63.41% and in mean absolute error (MAE) by up to 62.63% compared to a standard GP baseline. The proposed DME-GP framework provides a robust and reliable data-driven solution for advancing battery health monitoring systems.