A novel remaining useful life prediction framework for lithium-ion battery using grey model and particle filtering


An accurate remaining useful life (RUL) prediction method is significant to optimize the lithium-ion batteries' performances in an intelligent battery management system. Since the construction of battery models and the initialization of algorithms require a large amount of data, it is difficult for conventional methods to guarantee the RUL prediction accuracy when the available data are insufficient. To solve this problem, a synergy of sliding-window grey model (SGM) and particle filter (PF) is exploited to build an innovative framework for battery RUL prediction. The SGM is adopted to explore the modelling of battery capacity degradation, and it characterizes the capacity changes during the battery's life-time with a few data (eg, 8 data points). To promote the accuracy and traceability of prediction, the development coefficient of the SGM, which can dynamically reflect the capacity degradation, is extracted to update the state variables of state transition function in PF. Accordingly, the fusion of SGM and PF (SGM-PF) can extrapolate the changes of the capacity and realize RUL prediction using fewer data. Furthermore, the performances of SGM-PF are comprehensively validated using two types of batteries aged under different conditions. The RUL prediction results reveal that the SGM-PF framework can achieve precise and reliable predictions in different prediction horizons with as few as 8 data points, and it has prominent performance in accuracy and stability over contrastive methods, especially in long-term prognosis.

Publication DOI: https://doi.org/10.1002/er.5464
Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
Additional Information: This is the peer reviewed version of the following article: hen, L, Wang, H, Chen, J, et al. A novel remaining useful life prediction framework for lithium‐ion battery using grey model and particle filtering. Int J Energy Res. 2020; 1– 15., which has been published in final form at https://doi.org/10.1002/er.5464.  This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Uncontrolled Keywords: grey model,lithium-ion battery,particle filter,prediction,remaining useful life,Renewable Energy, Sustainability and the Environment,Nuclear Energy and Engineering,Fuel Technology,Energy Engineering and Power Technology
Publication ISSN: 0363-907X
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://onlinel ... 10.1002/er.5464 (Publisher URL)
PURE Output Type: Article
Published Date: 2020-07-01
Published Online Date: 2020-05-07
Accepted Date: 2020-03-26
Authors: Chen, Lin
Wang, Huimin
Chen, Jing
An, Jingjing
Ji, Bing
Lyu, Zhiqiang
Cao, Wenping (ORCID Profile 0000-0002-8133-3020)
Pan, Haihong



Version: Accepted Version

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