Lithium-ion Battery Remaining Useful Life Estimation Based on Nonlinear AR Model Combined with Degradation Feature

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 23, 2012
Datong Liu Yue Luo Yu Peng Xiyuan Peng Michael Pecht

Abstract

Long term prediction such as multi-step time series prediction is a challenging prognostics problem. This paper proposes an improved AR time series model called ND-AR model (Nonlinear Degradation AutoRegression) for Remaining Useful Life (RUL) estimation of lithium-ion batteries. The nonlinear degradation feature of the lithium- ion battery capacity degradation is analyzed and then the non-linear accelerated degradation factor is extracted to improve the linear AR model. In this model, the nonlinear degradation factor can be obtained with curve fitting, and then the ND-AR model can be applied as an adaptive data- driven prognostics method to monitor degradation time series data. Experimental results with CALCE battery data set show that the proposed nonlinear degradation AR model can realize satisfied prognostics for various lithium-ion batteries with low computing complexity.

How to Cite

Liu, D., Luo, Y., Peng, Y., Peng, X., & Pecht, M. (2012). Lithium-ion Battery Remaining Useful Life Estimation Based on Nonlinear AR Model Combined with Degradation Feature. Annual Conference of the PHM Society, 4(1). https://doi.org/10.36001/phmconf.2012.v4i1.2165
Abstract 874 | PDF Downloads 739

##plugins.themes.bootstrap3.article.details##

Keywords

Remaining Useful Life Estimation, Lithium-ion battery, time series, AR

References
Bhaskar Saha, Kai Goebel (2009). Modeling li-ion battery capacity depletion in a particle filtering framework. Annual Conference of the Prognostics and Health Management Society.
Jingliang Zhang, Jay Lee (2011). A review on prognostics and health monitoring of Li-ion battery. Journal of Power Sources, 196, 6007-6014.
Wei He, Nicholas Williard, Michael Osterman, Michael Pecht (2011). Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method. Journal of Power Sources, 196, 10314- 10321.
Goebel, B. Saha, A. Saxena, J. R. Celaya, J. P. Christophersen (2008). Prognostics in battery health management, IEEE Instrumentation &Measurement Magazine. 8, 33-40.
F. Rufus, S. Lee, A. Thakker (2008). Health Monitoring Algorithms for Space Application Batteries. In Proceedings of International Conference on Prognostics and Health Management.
Xiao-Sheng Si, Wenbin Wang, Chang-Hua Hu, Dong-Hua Zhou (2011). Remaining useful life estimation – A review on the statistical data driven approaches. European Journal of Operational Research, 213(1), 1-14.
Bhaskar Saha, Kai Goebel, Jon Christophersen (2009). Comparison of prognostic algorithms for estimating remaining useful life of batteries. Transactions of the Institute of Measurement and Control. 31, 293-308.
Bhaskar Saha, Kai Goebel, Scott Poll, Jon Christophersen (2009), Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework, IEEE Transactions on Instrumentation and Measurement, 58(2), 291- 297.
Enrico Zio , Giovanni Peloni (2011). Particle filtering prognostic estimation of the remaining useful life of nonlinear components, Reliability Engineering and System Safety, 96, 403-409.
Achmad Widodo, Min-Chan Shim,Wahyu Caesarendra, Bo- Suk Yang (2011). Intelligent prognostics for battery health monitoring based on sample entropy, Procedia Engineering, 14, 2707-2713
Jie Liu, Abhinav Saxena, Kai Goebel, Bhaskar Saha, and Wilson Wang (2010). An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries. Annual Conference of the Prognostics and Health Management Society.
Lijun Gao, Shengyi Liu, Roger A. Dougal (2002). Dynamic Lithium-Ion Battery Modelfor System Simulation. IEEE Transaction on Components and Packaging Technologies, 25(3), 495-505.
Jianqing Fan, Qiwei Yao (2003) Nonlinear Time Series: Nonparametric and parametric methods. SPRINGER, 12-123.
Akaike H (1974). A New Look at the Statistical Model Identification. IEEE Transactions on Automatic Control. 19, 716-723
B. Saha, K. Goebel (2007). Battery Data Set, NASA Ames Prognostics Data Repository, [http://ti.arc.nasa.gov/project/prognostic-data-repository], NASA Ames, Moffett Field, CA.
Section
Technical Research Papers

Most read articles by the same author(s)