Prognostic Algorithm for Remaining Useful Life of Battery Using Particle Filtering

Abstract

Achieving accurate and reliable remaining useful life (RUL) prediction of lithium-ion batteries is very vital for the effective and normal operation of battery-powered equipment's. A method to predict the remaining useful life (RUL) of lithium-ion batteries is being proposed in this paper. The capacity estimated is deployed to replace the measured value of the Particle Filter (PF). This is based on a Kendall rank correlation coefficient (KCCPF). This method is used to predict/determine the remaining useful life of batteries. Later we employ prognostic methods such as hybrid and data approaches. Hybrid approach uses the particle filter while data approach employs an autoregressive integrated average. The approximated result shows that the proposed method has high estimation accuracy and practical value.