Lithium-ion battery life prognostic health management system using particle filtering framework

Abstract

In this paper, a detailed implementation of a lithium-ion battery life prognostic system using a particle filtering framework is presented. A lumped parameter battery model is used to account for all the dynamic characteristics of the battery: a non-linear open-circuit voltage, current, temperature, cycle number, and time-dependent storage capacity. The internal processes of the battery are used to form the basis of this model. Statistical estimates of the noise in the system and the anticipated operational conditions are processed to provide estimates of the remaining useful life. The model is then subsequently used in the particle-filtering framework with a sequential importance resampling algorithm to predict the remaining useful life of the battery for individual discharge cycles as well as for the battery cycle life. The research presented in this paper provides the necessary steps towards a comprehensive battery health management solution for energy storage devices.