Robust Remaining Useful Lifetime Prediction for Lithium-Ion Batteries With Dual Gaussian Process Regression-Based Ensemble Strategies on Limited Sample Data

Abstract

Lithium-ion batteries have emerged as the primary power source for electric mobilities. Accurate remaining useful lifetime (RUL) prediction is required to ensure the safe operation of the batteries throughout their lifespan. This article proposes combination strategies that integrate two different Gaussian process regression (GPR) methods and model-based methods to enhance the robustness of the prediction. The first GPR is based on the forward extrapolation of the measured capacity sequence. The second GPR is based on the extrapolation of the measured feature and then inputs the predicted feature into a capacity estimation model. The first ensemble strategy is the weighted ensemble method, which uses the least squares method to determine the weighted coefficients. The second strategy is a more conservative method, which chooses the fastest degradation path between two basic methods at each prediction step. The third strategy is particle filter (PF), which combines the predicted data from different methods. The batteries aged by a real forklift aging profile and open access dataset are used to verify the proposed methods. The results of all methods based on different percentages of data are analyzed. The results show that individual methods may obtain different prediction results, while ensemble strategies have accurate and robust predictions. The PF for capacity-based and feature-based methods has the best performance with the absolute error of RUL less than 23 full equivalent cycles (FECs), error of prediction steps less than 1, and negligible simulation time for the forklift dataset.