Piecewise Model-Based Online Prognosis of Lithium-Ion Batteries Using Particle Filters

Abstract

Lithium-ion batteries are used as energy sources for energy storage systems, electric vehicles, consumer electronic devices and much more. Prediction of the remaining useful life (RUL) of such sources is vital to improve the safety and reliability of battery-powered systems. Even though several prognostic methods have been extensively explored for the RUL prediction of lithium-ion batteries, these methods are focused on adopting a single empirical / phenomenological degradation model which best describes the degradation behavior. However, certain lithium-ion battery materials exhibit two distinct degradation behaviors with an evident inflection point. In such cases, a single empirical model no longer holds good. Hence, we propose a piecewise degradation model along with a novel methodology to determine the inflection point. The proposed model is incorporated into a particle filter framework to predict the battery's degradation trajectories. The effectiveness of the proposed model is verified by adding a 50dB noise to the measurement data. The prognostic results of the proposed piecewise model are compared with the existing single empirical model. We use prediction error and execution time as the prognostic metrics for comparison.