Adaptive state of charge estimation for Li-ion batteries based on an unscented kalman filter with an enhanced battery model

Energies

Volumn 6, Issue 8, 2013, Pages 4134-4151

  He, Zhiwei ^a   Gao, Mingyu ^a   Wang, Caisheng ^b   Wang, Leyi ^b   Liu, Yuanyuan ^a  

^a HANGZHOU DIANZI UNIVERSITY   (China)

^b Wayne State University   (United States)

Author keywords

Battery; Online estimation; State of charge; Unscented Kalman filter

Indexed keywords

BANDPASS FILTERS; CHARGING (BATTERIES); ELECTRIC BATTERIES; ESTIMATION; IONS; KALMAN FILTERS; LITHIUM; LITHIUM-ION BATTERIES; NONLINEAR FILTERING; SECONDARY BATTERIES;

ACCURATE ESTIMATION; BATTERY; COMPUTATIONALLY EFFICIENT; INTERNAL RESISTANCE; ON-LINE ESTIMATION; STATE OF CHARGE; STATE-OF-CHARGE ESTIMATION; UNSCENTED KALMAN FILTER;

BATTERY MANAGEMENT SYSTEMS;

EID: 84884654699     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en6084134     Document Type: Article

References (34)

1. Larico, H.R.E.; Barbi, I. Three-phase push-pull DC-DC converter: Analysis, design, and experimentation. IEEE Trans. Ind. Electron. 2012, 12, 4629-4636.
2. Kutluay, K.; Çadirci, Y.; Özkazanç, Y.S. A new online state-of charge estimation and monitoring system for sealed lead-acid batteries in telecommunication power supplies. IEEE Trans. Ind. Electron. 2005, 5, 1315-1327.
3. Hu, Y.; Yurkovich, S. Battery State of Charge Estimation in Automotive Applications Using LPV Techniques. In Proceedings of 2010 American Control Conference, Baltimore, MD, USA, 30 June-2 July 2010.
4. Amjadi, Z.; Williamson, S.S. Power-electronics-based solutions for plug-in hybrid electric vehicle energy storage and management systems. IEEE Trans. Ind. Electron. 2010, 2, 608-616.
5. Ma, C.; Yang, Y. A battery-aware scheme for routing in wireless ad hoc networks. IEEE Trans. Ind. Electron. 2011, 8, 3919-3932.
6. Shahriairi, M.; Farrokhi, M. On-line state of health estimation of VRLA batteries using state of charge. IEEE Trans. Ind. Electron. 2013, 1, 191-202.
7. Yuan, S.; Wu, H.; Yin, C. State of charge estimation using the extended Kalman filter for battery management systems based on the ARX battery model. Energies 2013, 6, 444-470.
8. Urbain, M.; Raël, S.; Davat, B.; Desprez, P. State Estimation of a Lithium-Ion Battery through Kalman Filter. In Proceedings of IEEE Power Electronics Specialists Conference, Orlando, FL, USA, 17-21 June 2007.
9. Plette, G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. J. Power Sources 2004, 2, 262-276.
10. Plette, G.L. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 2: Simultaneous state and parameter estimation. J. Power Sources 2006, 2, 1369-1384.
11. Zhang, F.; Liu, G.; Fang, L.; Wang, H. Estimation of battery state of charge with H-infinity observer: Applied to a robot for inspecting power transmission lines. IEEE Trans. Ind. Electron. 2012, 2, 1086-1095.
12. Lee, Y.-S.; Wang, W.-Y.; Kuo, T.-Y. Soft computing for battery state-of-charge (BSOC) estimation in battery string systems. IEEE Trans. Ind. Electron. 2008, 1, 229-239.
13. Li, S.G.; Sharkh, S.M.; Walsh, F.C.; Zhang, C.N. Energy and battery management of a plug-in series hybrid electric vehicle using fuzzy logic. IEEE Trans. Veh. Technol. 2011, 8, 3571-3585.
14. Knauff, M.; Dafis, C.; Niebur, D. A New Battery Model for Use with an Extended Kalman Filter State of Charge Estimator. In Proceedings of 2010 American Control Conference, Baltimore, MD, USA, 30 June-2 July 2010.
15. Smith, K.A.; Rahn, C.D.; Wang, C.Y. Control oriented 1D electrochemical model of Lithium-ion battery. Energy Convers. Manag. 2007, 9, 2565-2578.
16. Bhangu, B.S.; Bentley, P.; Stone, D.A.; Bingham, C.M. Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles. IEEE Trans. Veh. Technol. 2005, 3, 783-794.
17. Coleman, M.; Lee, C.K.; Zhu, C.; Hurley, W.G. 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. 2007, 5, 2550-2557.
18. Sitterly, M.; Wang, L.Y.; Yin, G.; Wang, C. Enhanced identification of battery models for real-time battery management. IEEE Trans. Sustain. Energy 2011, 3, 300-308.
19. Kim, T.; Qiao, W. A hybrid battery model capable of capturing dynamic circuit characteristics and nonlinear capacity effects. IEEE Trans. Energy Conv. 2011, 4, 1172-1180.
20. Chen, L.-R.; Liu, C.-S.; Chen, J.-J. Improving phase-locked battery charger speed by using resistance-compensated technique. IEEE Trans. Ind. Electron. 2009, 4, 1205-1211.
21. Agarwal, V.; Uthaichana, K.; DeCarlo, R.A.; Tsoukalas, L.H. Development and validation of a battery model useful for discharging and charging power control and lifetime estimation. IEEE Trans. Energy Conv. 2010, 3, 821-835.
22. Carter, R.; Cruden, A.; Hall, P.J.; Zaher, A.S. An improved lead-acid battery pack model for use in power simulations of electric vehicles. IEEE Trans. Energy Conv. 2012, 1, 21-28.
23. Klein, R.; Chaturvedi, N.A.; Christensen, J.; Ahmed, J.; Findeisen, R.; Kojic, A. State Estimation of a Reduced Electrochemical Model of a Lithium-Ion Battery. In Proceedings of 2010 American Control Conference, Baltimore, MD, USA, 30 June-2 July 2010.
24. Liu, L.; Wang, L.Y.; Chen, Z.; Wang, C.; Lin, F.; Wang, H. Integrated system identification and state-of-charge estimation of battery systems. IEEE Trans. Energy Conv. 2013, 1, 12-23.
25. Xing, Y.; Ma, E.W.M.; Tsui, K.L.; Pecht, M. Battery management systems in electric and hybrid vehicles. Energies 2011, 4, 1840-1857.
26. He, W.; Williard, N.; Osterman, M.; Pecht, M. Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method. J. Power Sources 2011, 196, 10314-10321.
27. He, W.; Williard, N.; Chen, C.; Pecht, M. State of charge estimation for electric vehicle batteries using unscented kalman filtering. Microelectron. Reliab. 2013, 53, 840-847.
28. Julier, S.J.; Uhlmann, J.K.; Durrant, W.H.F. A New Approach for Filtering Nonlinear Systems. In Proceedings of the American Control Conference, Seattle, WA, USA, 21-23 June 1995.
29. Van der Merwe, R. Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models. Ph.D. Thesis, Oregon Health & Science University, Portland, OR, USA, 2004.
30. Turner, R.; Rasmussen, C.E. Model Based Learning of Sigma Points in Unscented Kalman Filtering. In Proceedings of Machine Learning for Signal Processing, Kittilä, Finland, 29 August-1 September 2010.
31. He, Z.; Gao, M.; Xu, J.; Liu, Y. Battery Model Parameters Estimation with the Sigma Point Kalman Filter. In Proceedings of 2009 International Conference on Artificial Intelligence and Computational Intelligence, Shanghai, China, 7-9 November 2009.
32. Haykin, S.O. Adaptive Filter Theory, 4th ed.; Prentice Hall: Englewood Cliffs, NJ, USA, 2001.
33. He, Z.; Liu, Y.; Gao, M.; Wang, C. A Joint Model and SOC Estimation Method for Lithium Battery Based on the Sigma Point KF. In Proceedings of 2012 IEEE Transportation Electrification Conference and Expo, Dearborn, MI, USA, 18-20 June 2012.
34. Dynamometer Driving Scheules Utilized at the National Vehicle and Fuel Emissions Laoratory. Available online: http://www.epa.gov/nvfel/testing/dynamometer.htm (accessed on 12 June 2012).