State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble. (June 2018)

• Record Type: Journal Article
• Authors: Yu, Jianbo
• Is Part Of: Reliability engineering & system safety. Volume 174(2018)
• Page Start: 82
• Page End: 95
• Publication Date: 2018-06
• Subjects: Lithium-ion battery -- State of health -- Empirical mode decomposition -- Logic regression -- Gaussian process regression; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2018.02.022
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: A Multiscale predictor is proposed for battery health prognostics. Empirical mode decomposition is used for decomposition of battery capacity. An integration of LR and GPR is proposed for remaining useful life prediction. The results on Lithium-ion battery illustrate effectiveness of the proposed method. Abstract: State of health (SOH) prediction plays a vital role in battery health prognostics. It is important to estimate the capacity of Lithium-ion battery for future cycle running. In this paper, a novel method is developed based on an integration of multiscale logic regression (LR) and Gaussian process regression (GPR) to tackle SOH estimation and prediction problem of Lithium-ion battery. Empirical mode decomposition is employed to decouple global degradation, local regeneration and various fluctuations in battery capacity time series. An LR model with varying moving window is utilized to fit the residuals (i.e., the global degradation trend). A GPR with the lag vector is developed to recursively estimate local regenerations and fluctuations. This design scheme captures the time-varying degradation behavior and reduces affections of local regeneration phenomenon in Lithium-ion batteries. The experimental results on Lithium-ion battery data from NASA Ames Prognostics Center of Excellence illustrate the potential applications of the proposed method as an effective tool for battery health prognostics. Graphical abstract: