State-of-Health Monitoring and Prediction of Lithium-Ion Battery Using Probabilistic Indication and State-Space Model

IEEE Transactions on Instrumentation and Measurement

Volumn 64, Issue 11, 2015, Pages 2937-2949

  Yu, Jianbo ^a  

^a TONGJI UNIVERSITY   (China)

Author keywords

Battery health prognostics; Bayesian inference; generative topographic mapping (GTM); remaining useful life (RUL) prediction; state space model (SSM)

Indexed keywords

BATTERY MANAGEMENT SYSTEMS; BAYESIAN NETWORKS; ELECTRIC BATTERIES; FORECASTING; INFERENCE ENGINES; LITHIUM ALLOYS; MAPPING; SECONDARY BATTERIES; STATE SPACE METHODS; SYSTEMS ENGINEERING;

BATTERY HEALTH; BAYESIAN INFERENCE; GENERATIVE TOPOGRAPHIC MAPPING; REMAINING USEFUL LIFE PREDICTIONS; STATE SPACE MODEL;

LITHIUM-ION BATTERIES;

EID: 84959502439     PISSN: 00189456     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIM.2015.2444237     Document Type: Article

References (41)

1. K. Goebel, B. Saha, A. Saxena, J. R. Celaya, and J. P. Christophersen, "Prognostics in battery health management," IEEE Instrum. Meas. Mag., vol. 11, no. 4, pp. 33-40, Aug. 2008.
2. B. E. Olivares, M. A. Cerda Munoz, M. E. Orchard, and J. F. Silva, "Particle-filtering-based prognosis framework for energy storage devices with a statistical characterization of state-of-health regeneration phenomena," IEEE Trans. Instrum. Meas., vol. 62, no. 2, pp. 364-376, Feb. 2013.
3. J. Yu, "Local and nonlocal preserving projection for bearing defect classification and performance assessment," IEEE Trans. Ind. Electron, vol. 59, no. 5, pp. 2363-2376, May 2012.
4. M. Shahriari and M. Farrokhi, "Online state-of-health estimation of VRLA batteries using state of charge," IEEE Trans. Ind. Electron., vol. 60, no. 1, pp. 191-202, Jan. 2013.
5. J. Kim and B. H. Cho, "State-of-charge estimation and state-of-health prediction of a Li-ion degraded battery based on an EKF combined with a per-unit system," IEEE Trans. Veh. Technol., vol. 60, no. 9, pp. 4249-4260, Nov. 2011.
6. P. E. Pascoe and A. H. Anbuky, "Standby power system VRLA battery reserve life estimation scheme," IEEE Trans. Energy Convers., vol. 20, no. 4, pp. 887-895, Dec. 2005.
7. Y.-H. Sun, H.-L. Jou, and J.-C. Wu, "Aging estimation method for leadacid battery," IEEE Trans. Energy Convers., vol. 26, no. 1, pp. 264-271, Mar. 2011.
8. W. Xian, B. Long, M. Li, and H. Wang, "Prognostics of lithium-ion batteries based on the Verhulst model, particle swarm optimization and particle filter," IEEE Trans. Instrum. Meas., vol. 63, no. 1, pp. 2-17, Jan. 2014.
9. M. V. Micea, L. Ungurean, G. N. Cârstoiu, and V. Groza, "Online stateof-health assessment for battery management systems," IEEE Trans. Instrum. Meas., vol. 60, no. 6, pp. 1997-2006, Jun. 2011.
10. S. Lee, H. Cui, M. Rezvanizaniani, and J. Ni, "Battery prognostics: SoC and SoH prediction," in Proc. ASME Int. Manuf. Sci. Eng. Conf. (MSEC), Notre Dame, IN, USA, Jun. 2012, pp. 689-695.
11. H. Blanke et al., "Impedance measurements on lead-acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles," J. Power Sour., vol. 144, no. 2, pp. 418-425, Jun. 2005.
12. M. Coleman, C. K. Lee, C. Zhu, and W. G. Hurley, "State-of-charge determination from EMF voltage estimation: Using impedance, terminal voltage, and current for lead-acid and lithium-ion batteries," IEEE Trans. Ind. Electron., vol. 54, no. 5, pp. 2550-2557, Oct. 2007.
13. J. Yu, "Health degradation detection and monitoring of lithium-ion battery based on adaptive learning method," IEEE Trans. Instrum. Meas., vol. 63, no. 7, pp. 1709-1721, Jul. 2014.
14. K. B. Hatzell, A. Sharma, and H. K. Fathy, "A survey of long-term health modeling, estimation, and control of lithium-ion batteries: Challenges and opportunities," in Proc. Amer. Control Conf., Montréal, QC, Canada, Jun. 2012, pp. 584-591.
15. M. Cugnet et al., "On lead-acid-battery resistance and crankingcapability estimation," IEEE Trans. Ind. Electron., vol. 57, no. 3, pp. 909-917, Mar. 2010.
16. B. S. Bhangu, P. Bentley, D. A. Stone, and C. M. Bingham, "Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles," IEEE Trans. Veh. Technol., vol. 54, no. 3, pp. 783-794, May 2005.
17. B. Saha, K. Goebel, S. Poll, and J. Christophersen, "An integrated approach to battery health monitoring using Bayesian regression and state estimation," in Proc. IEEE AUTOTESTCON, Sep. 2007, pp. 646-653.
18. B. Saha, K. Goebel, S. Poll, and J. Christophersen, "Prognostics methods for battery health monitoring using a Bayesian framework," IEEE Trans. Instrum. Meas., vol. 58, no. 2, pp. 291-296, Feb. 2009.
19. M. Charkhgard and M. Farrokhi, "State-of-charge estimation for lithium-ion batteries using neural networks and EKF," IEEE Trans. Instrum. Meas., vol. 57, no. 12, pp. 4178-4187, Dec. 2010.
20. S. Tang, C. Yu, X. Wang, X. Guo, and X. Si, "Remaining useful life prediction of lithium-ion batteries based on the Wiener process with measurement error," Energies, vol. 7, no. 2, pp. 520-547, Jan. 2014.
21. B. Saha et al., "Battery health management system for electric UAVs," in Proc. IEEE Aerosp. Conf., Mar. 2011, pp. 1-9.
22. J. Liu, A. Saxena, K. Goebel, B. Saha, and W. Wang, "An adaptive recurrent neural network for remaining useful life prediction of lithiumion batteries," in Proc. Annu. Conf. Prognostics Health Manage. Soc., Sep. 2010, pp. 1-8.
23. K. Goebel, B. Saha, and A. Saxena, "A comparison of three data-driven techniques for prognostics," in Proc. 62nd Meeting Soc. Mach. Failure Prevention Technol. (MFPT), Virginia Beach, VA, USA, May 2008, pp. 119-131.
24. A. Barré, B. Deguilhem, S. Grolleau, M. Gérard, F. Suard, and D. Riu, "A review on lithium-ion battery ageing mechanisms and estimations for automotive applications," J. Power Sour., vol. 241, no. 1, pp. 680-689, Nov. 2013.
25. J. Yu, "A nonlinear probabilistic method and contribution analysis for machine condition monitoring," Mech. Syst. Signal Process., vol. 37, nos. 1-2, pp. 293-314, May/Jun. 2013.
26. C. M. Bishop, M. Svensén, and C. K. I. Williams, "Developments of the generative topographic mapping," Neurocomputing, vol. 21, nos. 1-3, pp. 203-224, Nov. 1998.
27. C. M. Bishop, M. Svensén, and C. K. I. Williams, "The generative topographic mapping," Neural Comput., vol. 10, no. 1, pp. 215-234, Jan. 1998.
28. A. P. Dempster, M. N. Laird, and D. B. Rubin, "Maximum likelihood from incomplete data via the EM algorithm," J. Roy. Statist. Soc. B (Methodological), vol. 39, no. 1, pp. 1-38, 1977.
29. J. E. Jackson, A User's Guide to Principal Components. New York, NY, USA: Wiley, 1991.
30. J. M. Lucas and M. S. Saccucci, "Exponentially weighted moving average control schemes: Properties and enhancements," Technometrics, vol. 32, no. 1, pp. 1-12, Mar. 1990.
31. D. W. Hosmer and S. Lemeshow, Applied Logistic Regression, 2nd ed. New York, NY, USA: Wiley, 2000.
32. W. M. Houston and D. J. Woodruff, "Empirical Bayes estimates of parameters from the logistic regression model," Iowa City, ACT Res. Rep. Ser. 97-6, Jun./Aug. 1997.
33. K. E. Spezzaferro, "Applying logistic regression to maintenance data to establish inspection intervals," in Proc. Annu. Rel. Maintainability Symp., Jan. 1996, pp. 296-300.
34. M. S. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, "A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking," IEEE Trans. Signal Process., vol. 50, no. 2, pp. 174-188, Feb. 2002.
35. A. Doucet, S. Godsill, and C. Andrieu, "On sequential Monte Carlo sampling methods for Bayesian filtering," Statist. Comput., vol. 10, no. 3, pp. 197-208, Jul. 2000.
36. J. S. Liu and R. Chen, "Sequential Monte Carlo methods for dynamic systems," J. Amer. Statist. Assoc., vol. 93, no. 443, pp. 1032-1044, Sep. 1998.
37. J. Sun, H. Zuo, W. Wang, and M. G. Pecht, "Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance," Mech. Syst. Signal Process., vol. 28, pp. 585-596, Apr. 2012.
38. B. Saha and K. Goebel. (2007). Battery data set. NASA Ames Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA, USA. [Online]. Available: http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/
39. M. Dalal, J. Ma, and D. He, "Lithium-ion battery life prognostic health management system using particle filtering framework," Proc. Inst. Mech. Eng. O, J. Risk Rel., vol. 225, no. 1, pp. 81-90, Mar. 2011.
40. M. E. Tipping, "The relevance vector machine," in Advances in Neural Information Processing Systems, vol. 12. Cambridge, MA, USA: MIT Press, 2000, pp. 652-658
41. B. Saha, K. Goebel, and J. Christophersen, "Comparison of prognostic algorithms for estimating remaining useful life of batteries," Trans. Inst. Meas. Control, vol. 31, nos. 3-4, pp. 293-308, Jun. 2009.