Influence of parameter initialization on battery life prediction for online applications

Estimating remaining useful life (RUL) is a crucial part in a successful online monitoring system. Extrapolation of a degradation model based on particle filtering (PF) approach, which is implemented on state-space model, is a popular method to predict RUL. Taking into account the characteristics of state-space model, the initial setting of process and measurement noise has a great impact on the predicted result. This paper discusses the individual performance of two popular PFs, sequential importance resampling PF and auxiliary PF, when they come to different noise characteristics. Two groups of initial process and measurement noises were set to compare the predicted performance between these two PFs. The prediction of battery RUL was demonstrated in this paper as a case study. The comparative results were used for reference to other-related degradation components or system using PFs-based prediction.