Interacting multiple model particle filter for prognostics of lithium-ion batteries
Authors:
- Xiaohong SuXSXiaohong Su
- Shuai WangSWShuai Wang
- Michael PechtMPMichael Pecht
- Lingling ZhaoLZLingling Zhao
- Zhe YeZYZhe Ye
- 1 March 2017
- journal article
- Published by Elsevier in Microelectronics Reliability
- Vol. 70, 59-69
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (61175027, 61305013)
- Fundamental Research Funds for the Central Universities (HIT.NSRIF.2014071)
- Research Fund for the Doctoral Program of Higher Education of China (20132302120044)
This publication has 33 references indexed in Scilit:
- A review on prognostics and health monitoring of Li-ion batteryJournal of Power Sources, 2011
- A regularized auxiliary particle filtering approach for system state estimation and battery life predictionSmart Materials and Structures, 2011
- Grey system theory-based models in time series predictionExpert Systems with Applications, 2010
- Comparison of prognostic algorithms for estimating remaining useful life of batteriesTransactions of the Institute of Measurement and Control, 2009
- Valve Regulated Lead Acid battery float service life estimation using a Kalman filterJournal of Power Sources, 2009
- A review of the main parameters influencing long-term performance and durability of PEM fuel cellsJournal of Power Sources, 2008
- Modeling capacity fade in lithium-ion cellsJournal of Power Sources, 2005
- Interacting multiple model particle filterIEE Proceedings - Radar, Sonar and Navigation, 2003
- Factors that affect cycle-life and possible degradation mechanisms of a Li-ion cell based on LiCoO2Journal of Power Sources, 2002
- A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingIEEE Transactions on Signal Processing, 2002