Prediction of Remaining Useful Life for Lithium-Ion Batteries Using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise for Feature Analysis, and Bidirectional Long Short-Term Memory Coupled with a Gaussian Process Regression Model

Abstract

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is a challenging task, with significant implications for managing battery usage risks and ensuring equipment stability. However, the phenomenon of capacity regeneration and the lack of confidence interval expression result in imprecise predictions. To tackle these challenges, this article proposes a novel method for predicting RUL by optimizing health features (HFs) and integrating multiple models. First, multiple HFs are collected from the charging curves, and the fusion HF is optimized by kernel principal component analysis. To eliminate local fluctuations caused by capacity regeneration effects, the complete ensemble empirical mode decomposition with adaptive noise is employed to decompose the fusion HF. Second, to address the issue of lacking confidence interval expression, a hybrid model is proposed by integrating bidirectional long short-term memory neural network with Gaussian process regression for effectively capturing the lithium-ion battery capacity-declining trend and accurately predicting the RUL. Finally, the proposed model's effectiveness is validated by comparing it with several other models using National Aeronautics and Space Administration and Center for Advanced Life Cycle Engineering datasets. The results indicate that this model achieves a root mean square error of 0.0023 and a mean absolute error of 0.0058, demonstrating significant improvements in predictive accuracy for RUL with high reliability.