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A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft

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  • Jin, Guang
  • Matthews, David E.
  • Zhou, Zhongbao

Abstract

The paper presents a Bayesian framework consisting of off-line population degradation modeling and on-line degradation assessment and residual life prediction for secondary batteries in the field. We use a Wiener process with random drift, diffusion coefficient and measurement error to characterize the off-line population degradation of secondary battery capacity, thereby capturing several sources of uncertainty including unit-to-unit variation, time uncertainty and stochastic correlation. Via maximum likelihood, and using observed capacity data with unknown measurement error, we estimate the parameters in this off-line population model. To achieve the requirements for on-line degradation assessment and residual life prediction, we exploit a particle filter-based state and static parameter joint estimation method, by which the posterior degradation model is updated iteratively and the degradation state of an individual battery is estimated at the sametime.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:reensy:v:113:y:2013:i:c:p:7-20
    DOI: 10.1016/j.ress.2012.12.011
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    References listed on IDEAS

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    4. Wang, Xiao, 2010. "Wiener processes with random effects for degradation data," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 340-351, February.
    5. Nicholas G. Polson & Jonathan R. Stroud & Peter Müller, 2008. "Practical filtering with sequential parameter learning," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(2), pages 413-428, April.
    6. Chookah, M. & Nuhi, M. & Modarres, M., 2011. "A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatigue," Reliability Engineering and System Safety, Elsevier, vol. 96(12), pages 1601-1610.
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