Scientific Reports, Volume 15, 3311, 2025, doi.org/10.1038/s41598-025-86511-z

Remaining Useful Life Prediction of Lithium-ion Batteries Using a Novel Particle Flow Filter Framework With Grey Model

Wang Shuai¹, Li Yiting¹, Zhou Shoubin², Chen Lifei¹, and Michael Pecht^3
1 Digital Fujian Internet-of-Things Laboatory of Environmental Monitoring, College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, Fujian, China.
² Huafu Hight Technology Energy Storage Co, Yangzhou 225699, China.
³ CALCE University of Maryland, College Park, MD 20742, USA.

For more information about this article and related research, please contact Prof. Michael Pecht.

Abstract:

Remaining useful life (RUL) prediction is a crucial aspect of the prognostics health management of lithium-ion batteries (LIBs). Owing to the influence of resampling technology, particle degradation is often observed in the particle filter-based RUL prediction of LIBs, resulting in a low prediction accuracy and large uncertainty. In this paper, a novel particle flow filter with the grey model method (GM-PFF) is proposed to forecast the RUL and state of health of batteries. First, the least squares method is employed to obtain the initial values for double exponential empirical model parameters. Subsequently, the grey model is used to predict the current cycle capacity of LIBs as an observation value for the particle flow filter, solving the inaccurate estimation problem of the state of particle flow filter observation values, and the particle flow filter method is employed to update model parameters. Finally, a test dataset is divided into early, middle, and late stages to predict the RUL of LIBs and obtain the probability distributions. On the CALCE and NASA PCoE LIB dataset, GM-PFF reduces RMSE by 1% compared to PFF, exhibiting a higher prediction accuracy and effectively addressing the particle degradation problem.

This article is available for free online here.