Abstract:

A method for remaining useful performance (RUP) analysis for lithium-ion batteries is presented using Dempster- Shafer theory (DST) and Bayesian Monte Carlo (BMC). First, an empirical model is developed, which can provide a good fit to the battery fade data. Then, the parameters of the empirical model are initialized by combining sets of training data based on DST. When data become available through battery monitoring, the model parameters are updated by the BMC to manage the uncertainties in the degradation process. Once the model converges to the observed degradation process, it can be propagated to the acceptable performance threshold to predict the RUP of batteries. The proposed approach is validated using experimental data.