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. 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.