Particle-Filtering-Based Prognosis Framework for Energy Storage Devices With a Statistical Characterization of State-of-Health Regeneration Phenomena

This paper presents the implementation of a particle-filtering-based prognostic framework that allows estimating the state of health (SOH) and predicting the remaining useful life (RUL) of energy storage devices, and more specifically lithium-ion batteries, while simultaneously detecting and isolating the effect of self-recharge phenomena within the life-cycle model. The proposed scheme and the statistical characterization of capacity regeneration phenomena are validated through experimental data from an accelerated battery degradation test and a set of ad hoc performance measures to quantify the precision and accuracy of the RUL estimates. In addition, a simplified degradation model is presented to analyze and compare the performance of the proposed approach in the case where the optimal solution (in the mean-square-error sense) can be found analytically.

[1]  Stephen J. Engel,et al.  Prognostics, the real issues involved with predicting life remaining , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[2]  Phl Peter Notten,et al.  REVIEW ARTICLE: State-of-the-art of battery state-of-charge determination , 2005 .

[3]  Richard Lee Hartmann,et al.  An Aging Model for Lithium-Ion Cells , 2008 .

[4]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[5]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[6]  M. Ichimura,et al.  Synergistic Effect of Charge/Discharge Cycle and Storage in Degradation of Lithium-ion Batteries for Mobile Phones , 2005, INTELEC 05 - Twenty-Seventh International Telecommunications Conference.

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

[8]  P.E. Pascoe,et al.  Standby VRLA battery reserve life estimation , 2004, INTELEC 2004. 26th Annual International Telecommunications Energy Conference.

[9]  George J. Vachtsevanos,et al.  A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .

[10]  Ralph E. White,et al.  Parameter Estimation and Life Modeling of Lithium-Ion Cells , 2008 .

[11]  Liu Qiao,et al.  Automotive battery management systems , 2008, 2008 IEEE AUTOTESTCON.

[12]  George J. Vachtsevanos,et al.  Impact of Input Uncertainty on Failure Prognostic Algorithms: Extending the Remaining Useful Life of Nonlinear Systems , 2010 .

[13]  Bin Zhang,et al.  A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection , 2011, IEEE Transactions on Industrial Electronics.

[14]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[15]  Krishna R. Pattipati,et al.  System Identification and Estimation Framework for Pivotal Automotive Battery Management System Characteristics , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Christophe Andrieu,et al.  Sequential Monte Carlo Methods for Optimal Filtering , 2001, Sequential Monte Carlo Methods in Practice.

[17]  Nando de Freitas,et al.  An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.

[18]  Liang Chu,et al.  Investigation of cycle life of li-ion power battery pack based on LV-SVM , 2011, 2011 International Conference on Mechatronic Science, Electric Engineering and Computer (MEC).

[19]  B. Mascialino,et al.  Application of statistical methods for the comparison of data distributions , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[20]  George Vachtsevanos,et al.  Novel metrics and methodologies for the verification and validation of prognostic algorithms , 2011, 2011 Aerospace Conference.

[21]  Andreas Jossen,et al.  Methods for state-of-charge determination and their applications , 2001 .

[22]  Yue Yuan,et al.  Temperature effect on electric vehicle battery cycle life in Vehicle-to-grid applications , 2010, CICED 2010 Proceedings.

[23]  P.H.L. Notten,et al.  Adaptive Battery Management Systems for the new generation of Electrical Vehicles , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[24]  R. Huggins.  Advanced Batteries: Materials Science Aspects , 2008 .

[25]  X. Rong Li,et al.  Distributed active learning with application to battery health management , 2011, 14th International Conference on Information Fusion.

[26]  Michael Osterman,et al.  Remaining useful performance analysis of batteries , 2011, 2011 IEEE Conference on Prognostics and Health Management.

[27]  S. Rodrigues,et al.  A review of state-of-charge indication of batteries by means of a.c. impedance measurements , 2000 .

[28]  Lucy Y. Pao,et al.  Variance estimation and ranking of Gaussian mixture distributions in target tracking applications , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[29]  A. Salkind,et al.  Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology , 1999 .

[30]  Bin Zhang,et al.  Application of Blind Deconvolution Denoising in Failure Prognosis , 2009, IEEE Transactions on Instrumentation and Measurement.

[31]  Sankalita Saha,et al.  Evaluating prognostics performance for algorithms incorporating uncertainty estimates , 2010, 2010 IEEE Aerospace Conference.

[32]  Bin Zhang,et al.  Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering , 2011, IEEE Transactions on Industrial Electronics.

[33]  Qiang Miao,et al.  Prognostics and health monitoring for lithium-ion battery , 2011, Proceedings of 2011 IEEE International Conference on Intelligence and Security Informatics.

[34]  A. Thakker,et al.  Health monitoring algorithms for space application batteries , 2008, 2008 International Conference on Prognostics and Health Management.

[35]  Sun Zechang,et al.  A new SOH prediction concept for the power lithium-ion battery used on HEVs , 2009, 2009 IEEE Vehicle Power and Propulsion Conference.

[36]  D.A. Stone,et al.  State-of-charge and state-of-health prediction of lead-acid batteries for hybrid electric vehicles using non-linear observers , 2005, 2005 European Conference on Power Electronics and Applications.

[37]  B. Saha,et al.  Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques , 2008, 2008 IEEE Aerospace Conference.

[38]  F. Huet.  A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries , 1998 .