Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena

This paper presents the implementation of a particle-filtering-based prognostic framework that allows estimating the state of health (SOH) and predicting the remaining useful life (RUL) of energy storage devices, and more specifically lithium-ion batteries, while simultaneously detecting and isolating the effect of self-recharge phenomena within the life-cycle model. The proposed scheme and the statistical characterization of capacity regeneration phenomena are validated through experimental data from an accelerated battery degradation test and a set of ad hoc performance measures to quantify the precision and accuracy of the RUL estimates. In addition, a simplified degradation model is presented to analyze and compare the performance of the proposed approach in the case where the optimal solution (in the mean-square-error sense) can be found analytically.