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State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble

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  • Yu, Jianbo

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.

Suggested Citation

  • Yu, Jianbo, 2018. "State of health prediction of lithium-ion batteries: Multiscale logic regression and Gaussian process regression ensemble," Reliability Engineering and System Safety, Elsevier, vol. 174(C), pages 82-95.
    • Handle: RePEc:eee:reensy:v:174:y:2018:i:c:p:82-95
    DOI: 10.1016/j.ress.2018.02.022
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    References listed on IDEAS

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    1. Jin, Guang & Matthews, David E. & Zhou, Zhongbao, 2013. "A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft," Reliability Engineering and System Safety, Elsevier, vol. 113(C), pages 7-20.
    2. Wang, Xiaolin & Balakrishnan, Narayanaswamy & Guo, Bo, 2014. "Residual life estimation based on a generalized Wiener degradation process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 13-23.
    3. Zheng, Xiujuan & Fang, Huajing, 2015. "An integrated unscented kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 74-82.
    4. Ng, Selina S.Y. & Xing, Yinjiao & Tsui, Kwok L., 2014. "A naive Bayes model for robust remaining useful life prediction of lithium-ion battery," Applied Energy, Elsevier, vol. 118(C), pages 114-123.
    5. Hu, Yang & Baraldi, Piero & Di Maio, Francesco & Zio, Enrico, 2015. "A particle filtering and kernel smoothing-based approach for new design component prognostics," Reliability Engineering and System Safety, Elsevier, vol. 134(C), pages 19-31.
    6. Xu, Xin & Chen, Nan, 2017. "A state-space-based prognostics model for lithium-ion battery degradation," Reliability Engineering and System Safety, Elsevier, vol. 159(C), pages 47-57.
    7. Shengjin Tang & Chuanqiang Yu & Xue Wang & Xiaosong Guo & Xiaosheng Si, 2014. "Remaining Useful Life Prediction of Lithium-Ion Batteries Based on the Wiener Process with Measurement Error," Energies, MDPI, vol. 7(2), pages 1-28, January.
    8. Xi, Zhimin & Jing, Rong & Wang, Pingfeng & Hu, Chao, 2014. "A copula-based sampling method for data-driven prognostics," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 72-82.
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