A self-cognizant dynamic system approach for prognostics and health management

Journal of Power Sources

Volumn 278, Issue , 2015, Pages 163-174

  Bai, Guangxing ^a   Wang, Pingfeng ^a   Hu, Chao ^b  

^a Wichita State University   (United States)

^b UNIVERSITY OF MARYLAND   (United States)

Author keywords

Dynamic systems; Kalman filter State of health (SoH); Lithium ion battery; Prognostics and health management; State of charge (SoC)

Indexed keywords

ARTIFICIAL INTELLIGENCE; CHARGING (BATTERIES); COMPLEX NETWORKS; DYNAMIC MODELS; DYNAMICAL SYSTEMS; ELECTRIC BATTERIES; KALMAN FILTERS; LITHIUM ALLOYS; LITHIUM BATTERIES; SECONDARY BATTERIES; STATE SPACE METHODS; SYSTEMS ENGINEERING;

DUAL EXTENDED KALMAN FILTER; DYNAMIC SYSTEM MODELING; ENGINEERING DISCIPLINES; EXPERIMENTAL APPLICATION; LITHIUM-ION BATTERY; PROGNOSTICS AND HEALTH MANAGEMENTS; STATE OF HEALTH; STATE-OF-CHARGE;

BATTERY MANAGEMENT SYSTEMS;

EID: 84919608143     PISSN: 03787753     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpowsour.2014.12.050     Document Type: Article

References (38)

1. J. Zhang, and J. Lee A review on prognostics and health monitoring of Li-ion battery J. Power Sources 196 15 2011 6007 6014
2. F. Serdio, E. Lughofer, K. Pichler, T. Buchegger, and M. Pichler Multivariate fault detection using vector autoregressive moving average and orthogonal transformation in residual space Annual Conference of the Prognostics and Health Management (PHM) Society 2013 2013
3. K.T. Chau, K.C. Wu, and C.C. Chan A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system Energy Convers. Manag. 45 2004 1681 1692
4. W.X. Shen, C.C. Chan, E.W.C. Lo, and K.T. Chau Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles IEEE Trans. Ind. Electron. 49 2004 2002 677 684
5. T. Wu, M. Wang, Q. Xiao, and X. Wang The SOC estimation of power Li-ion battery based on ANFIS model Smart Grid Renew. Energy (SGRE) 1 2012 51 55
6. M. Shahriari, and M. Farrokhi Online state-of-health estimation of VRLA batteries using state of charge IEEE Trans. Ind. Electron. 60 1 2013 191 202
7. Y.S. Lee, W.Y. Wang, and T.Y. Kuo Soft computing for battery state-of-charge (BSOC) estimation in battery string systems IEEE Trans. Ind. Electron. 55 1 2008 229 239
8. W.X. Shen, K.T. Chau, C.C. Chan, and E.W.C. Lo Neural network-based residual capacity indicator for nickel-metal hydride batteries in electric vehicles IEEE Trans. Veh. Technol. 54 5 2005 1705 1712
9. D.T. Lee, S.J. Shiah, C.M. Lee, and Y.C. Wang State-of-charge estimation for electric scooters by using learning mechanisms IEEE Trans. Veh. Technol. 56 2 2007 544 556
10. T. Hansen, and C.J. Wang Support vector based battery state of charge estimator J. Power Sources 141 2005 351 358
11. S. Das, and C. Willhauck Fatigue life prediction using multivariate Gaussian process 49th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Schaumburg, IL, April 7-10, 2008 2008
12. P. Boskoski Bearing fault prognostics based on signal complexity and Gaussian process models 2012 IEEE Conference on Prognostics and Health Management (PHM), Denver, CO June 18-21, 2012
13. A.E. Gelfand, and S.K. Sahu On Markov chain Monte Carlo acceleration J. Comput. Graph. Stat. 3 1994 261 276
14. D. An, J.H. Choi, and N.H. Kim Identification of correlated damage parameters under noise and bias using Bayesian inference Struct. Health Monit. 11 3 May, 2012 293 303
15. X. Guan, Y. Liu, A. Saxena, J. Celaya, and K. Goebel Entropy-based probabilistic fatigue damage prognosis and algorithmic performance comparison Annual Conference of the Prognostics and Health Management Society, San Diego, CA September 27-October 1, 2009
16. G.L. Plett Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - part 1. Background J. Power Sources 134 2004 252 261
17. G.L. Plett Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - part 2. Modeling and identification J. Power Sources 134 2004 262 276
18. G.L. Plett Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs - part 3. State and parameter estimation J. Power Sources 134 2004 277 292
19. M. Charkhgard, and M. Farrokhi State-of-charge estimation for lithium-ion batteries using neural network and EKF IEEE Trans. Ind. Electron. 57 12 2010 4178 4187
20. B. Saha, K. Goebel, S. Poll, and J. Christophersen Prognostics methods for battery health monitoring using a Bayesian framework IEEE Trans. Instr. Meas. 58 2009 291 296
21. E.A. Wan, and A.T. Nelson "Chapter 5: Dual Extended Kalman Filter Methods," Appears in Kalman Filtering and Neural Network Haykin, S 2001 John Wiley and Sons New York 123 174
22. G. Welch, and G. Bishop An Introduction to the Kalman Filter 2001 (Notes of ACM SIGGRAPH tutorial on the Kalman filter)
23. R.J. Williams, and D. Zipser A learning algorithm for continually running fully recurrent neural networks Neural Comput. 1 2 1989 270 280
24. A.K. Jain, and J. Mao Artificial neural networks: a tutorial Computer 29 3 1996 31 44
25. S. Haykin "Chapter 6: Multilayer Perceptrons," Appears in Neural Networks: a Comprehensive Foundation 1994 MacMillan College Publishing Company New York 138 235
26. S. Piller, M. Perrin, and A. Jossen Methods for state-of-charge determination and their applications J. Power Sources 96 2001 113 120
27. N. Williard, B. Sood, M. Osterman, and M. Pecht Disassembly methodology for conducting failure analysis on lithium-ion batteries J. Material Sci. Material Electron. 22 2011 1616 1630
28. N. Williard, W. He, and M. Pecht Model based battery management system for condition based maintenance MFPT 2012 Proceedings: the Prognostics and Health Management Solutions Conference, Dayton, OH April 24-26, 2012
29. F. Huet A review of impedance measurement for determination of state-of-charge or state-of-health of secondary battery J. Power Sources 70 1998 59 69
30. S. Rodrigues, N. Munichandraiah, and A.K. Shukla A review of state-of-charge indication of batteries by means of ac impedance measurements J. Power Sources 87 2000 12 20
31. W. He, N. Williard, M. Osterman, and M. Pecht Prognostics of lithium-ion batteries based on Dempster CShafer theory and the Bayesian Monte Carlo method J. Power Sources 196 2011 10314 10321
32. C. Hu, B.D. Youn, and J. Chung A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation Appl. Energy 92 2011 694 704
33. W. He, N. Williard, C.C. Chen, and M. Pecht State of charge estimation for electric vehicle batteries using unscented Kalman filtering Microelectron. Reliab. 196 2011 10314 10321
34. E. Meissner, and G. Richter Battery monitoring and electrical energy management precondition for future vehicle electric power systems J. Power Sources 116 1-2 2013 79 98
35. G. Bai, P. Wang, C. Hu, and M. Pecht A generic model-free approach for lithium-ion battery health management Appl. Energy 135 2014 247 260
36. C. Hu, G. Jain, P. Zhang, C. Schmidt, P. Gomadam, and T. Gorka Data-driven approach based on particle swarm optimization and K-nearest neighbor regression for estimating capacity of lithium-ion battery Appl. Energy 129 2014 49 55
37. C. Hu, G. Jain, P. Tamirisa, and T. Gorka Method for estimating capacity and predicting remaining useful life of lithium-ion battery Appl. Energy 126 2014 182 189
38. B. Saha, and K. Goebel "Battery Data Set", NASA Ames Prognostics Data Repository 2007 NASA Ames Moffett Field, CA http://ti.arc.nasa.gov/project/prognostic-data-repository