Remaining Useful Life Prediction of Lithium-Ion Batteries: A Hybrid Approach of Grey–Markov Chain Model and Improved Gaussian Process
Mingye Zhu, Quan Ouyang, Yong Wan, Zhisheng Wang
Abstract
Accurate prediction of the lithium-ion battery’s future capacity and remaining useful life (RUL) is of great significance to battery health management. To the best of our knowledge, most of the existing methods only focus on capturing the degradation trend of the battery, which results in an inaccurate prediction because of the occasional capacity regeneration phenomena in practice. To improve the prediction precision, a hybrid approach of the Grey–Markov chain model (GMCM) and the improved Gaussian process is proposed in this article. Firstly, the historical capacity data is decomposed into two components, namely, the degradation trend and regeneration phenomena, by ensemble empirical mode decomposition. Then, the GMCM method and the multioutput mixture of Gaussian processes are developed for degradation and regeneration prediction, respectively. Lastly, an online calibration strategy is proposed for further prediction accuracy improvement. Extensive experiments are established to validate the proposed GPGM approach, with the results demonstrating that the proposed GPGM consistently outperforms other counterpart methods in terms of one-step and multistep prediction.