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

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. A case study of some Li-ion type secondary batteries not only shows the effectiveness of our method, but also provides some useful insights regarding the necessity of on-line updating and the apparent differences between the population and individual unit degradation modeling and assessment problems.