Prognostics of Lithium-ion batteries based on state space modeling with heterogeneous noise variance

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalNot applicablepeer-review

8 Scopus Citations
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Original languageEnglish
Pages (from-to)1-8
Journal / PublicationMicroelectronics Reliability
Early online date3 Jun 2017
Publication statusPublished - Aug 2017

Abstract

Prognostics and health management of lithium-ion batteries, especially their remaining useful life (RUL) prediction, has attracted much attention in recent years because accurate battery RUL prediction is beneficial to ensuring high reliability of lithium-ion batteries for providing power sources for many electronic products. In the common state space modeling of battery RUL prediction, noise variances are usually assumed to be fixed. However, noise variances have great influence on state space modeling. If noise variances are too small, it takes long time for initial guess states to approach true states, and thus estimated states and measurements are biased. If noise variances are too large, state space modeling based filtering will suffer divergence. Besides, even though a same type of lithium-ion batteries are investigated, their degradation paths vary quite differently in practice due to unit-to-unit variation, ambient temperature and other working conditions. Consequently, heterogeneity of noise variances should be taken into consideration in state space modeling so as to improve RUL prediction accuracy. More importantly, noise variances should be posteriorly updated by using up-to-date battery capacity degradation measurements. In this paper, an efficient prognostic method for battery RUL prediction is proposed based on state space modeling with heterogeneity of noise variances. 26 lithium-ion batteries degradation data are used to illustrate how the proposed prognostic method works. Some comparisons with other common prognostic methods are conducted to show the superiority of the proposed prognostic method.