Microelectronics Reliability, Volume 53, Issue 6, Pages 805-810, June 2013

Remaining useful life prediction of lithium-ion battery with unscented particle filter technique

Qiang Miao a,*, Lei Xie a, Hengjuan Cui a, Wei Liang a, Michael Pecht b
a School of Mechanical, Electronic and Industrial Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
b Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20740, USA


Accurate prediction of the remaining useful life of a faulty component is important to the prognosis and health management of a system. It gives operators information about when the component should be replaced. In recent years, a lot of research has been conducted on battery reliability and prognosis, especially the remaining useful life prediction of the lithium-ion batteries. Particle filter (PF) is an effective method for sequential signal processing. It has been used in many areas, including computer vision, target tracking, and robotics. However, the accuracy of the PF is not high. This paper introduces an improved PF algorithm-unscented particle filter (UPF) into the battery remaining useful life prediction. First, PF algorithm and UPF algorithm are described separately. Then, a degradation model is built based on the understanding of lithium-ion batteries. Finally, the prediction results can be obtained using the degradation model and the UPF algorithms. According to the analysis results, it can be seen that UPF can predict the actual RUL with an error less than 5%.

Complete article is available from the publisher and to the CALCE Consortium Members.

[Home Page] [Articles Page]
Copyright © 2013 by CALCE and the University of Maryland, All Rights Reserved