Storage battery remaining useful life prognosis using improved unscented particle filter

Storage battery is one of the most important power sources in portable devices, marine systems, automotive vehicles, aerospace systems, and so on. For this kind of battery, it is essential to prognose its remaining useful life before its end of life, which would reduce some unnecessary sudden disasters caused by battery failure. In this article, we propose an improved unscented particle filter method for prognosing the remaining useful life of storage battery, in which the sigma samples of unscented transformation in traditional unscented particle filter are generated by singular value decomposition, and then, those sigma points are propagated by the standard unscented Kalman filter to generate a sophisticated proposal distribution. When both improved unscented particle filter and unscented particle filter methods were used for prognosing the remaining useful life of storage battery, it shows that the performance of improved unscented particle filter is better than unscented particle filter; the proposed method is more robust in remaining useful life prognosis procedure.

[1]  IL-Song Kim,et al.  A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer , 2010, IEEE Transactions on Power Electronics.

[2]  M. Pecht,et al.  Physics-of-failure: an approach to reliable product development , 1995, IEEE 1995 International Integrated Reliability Workshop. Final Report.

[3]  Qiang Miao,et al.  Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model , 2013 .

[4]  Jie Liu,et al.  A regularized auxiliary particle filtering approach for system state estimation and battery life prediction , 2011 .

[5]  Kung-Sik Chan,et al.  Reduced rank stochastic regression with a sparse singular value decomposition , 2012 .

[6]  Ji-Won Choi,et al.  Issue and challenges facing rechargeable thin film lithium batteries , 2008 .

[7]  Zhongbao Zhou,et al.  A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries in spacecraft , 2013, Reliab. Eng. Syst. Saf..

[8]  George Vachtsevanos,et al.  A Particle Filtering Framework for Failure Prognosis , 2005 .

[9]  David He,et al.  Lithium-ion battery life prognostic health management system using particle filtering framework , 2011 .

[10]  Jie Liu,et al.  Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm , 2013, Neural Computing and Applications.

[11]  S. Raël,et al.  Physical characterization of the charging process of a Li-ion battery and prediction of Li plating by electrochemical modelling , 2014 .

[12]  Nando de Freitas,et al.  The Unscented Particle Filter , 2000, NIPS.

[13]  Bo-Hyung Cho,et al.  Li-Ion Battery SOC Estimation Method Based on the Reduced Order Extended Kalman Filtering , 2006 .

[14]  Yuanyuan Liu,et al.  Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model , 2013 .

[15]  Liang Tang,et al.  Risk-Sensitive Particle-Filtering-based Prognosis Framework for Estimation of Remaining Useful Life in Energy Storage Devices< , 2010 .

[16]  Enrico Zio,et al.  Particle filtering prognostic estimation of the remaining useful life of nonlinear components , 2011, Reliab. Eng. Syst. Saf..

[17]  O. Payne,et al.  An unscented particle filter for GMTI tracking , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[18]  Wei Liang,et al.  Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..

[19]  Yong Rui,et al.  Better proposal distributions: object tracking using unscented particle filter , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[20]  Yu Peng,et al.  Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression , 2013, Microelectron. Reliab..

[21]  Kwok-Leung Tsui,et al.  An ensemble model for predicting the remaining useful performance of lithium-ion batteries , 2013, Microelectron. Reliab..

[22]  Gene H. Golub,et al.  Singular value decomposition and least squares solutions , 1970, Milestones in Matrix Computation.

[23]  Herbert L Case,et al.  Calendar- and cycle-life studies of advanced technology development program generation 1 lithium-ion batteries , 2002 .

[24]  Jie Gu,et al.  Prognostics implementation of electronics under vibration loading , 2007, Microelectron. Reliab..

[25]  M. Pecht,et al.  A Wireless Sensor System for Prognostics and Health Management , 2010, IEEE Sensors Journal.

[26]  M.G. Pecht,et al.  Prognostics and health management of electronics , 2008, IEEE Transactions on Components and Packaging Technologies.

[27]  Qi Cheng,et al.  A new unscented particle filter , 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

[28]  M. Pecht,et al.  rognostics of lithium-ion batteries based on Dempster – Shafer theory and the ayesian Monte Carlo method , 2011 .