Ensemble Remaining Useful Life Prediction for Lithium-Ion Batteries With the Fusion of Historical and Real-Time Degradation Data

Abstract

Remaining useful life (RUL) prediction is a critical task in prognostics and health management. The performances of traditional RUL prediction approaches for lithium-ion batteries are usually affected by the uncertainties involved in the data analysis and model selection. This paper proposes an ensemble prognostic approach under the particle filter (PF) framework to improve the prediction accuracy in consideration of the uncertainties. In PF algorithm, an optimal weights initialization method is proposed with the comprehensive consideration of model bias and variance, and a novel weighting scheme is proposed to optimize the ensemble model performance by assigning time-varying and degradation-dependent weights with the fusion of historical and real-time degradation data. Besides, a data noise quantification method is proposed and applied in the PF algorithm to solve the hyperparameter setting problem. The effectiveness of the proposed approach is illustrated through the real datasets obtained from two types of lithium-ion batteries.