Fading Kalman filter-based real-time state of charge estimation in LiFePO4 battery-powered electric vehicles

Applied Energy

Volumn 169, Issue , 2016, Pages 40-48

  Lim, KaiChin ^a   Bastawrous, Hany Ayad ^a,b   Duong, Van Huan ^a   See, Khay Wai ^a   Zhang, Peng ^a   Dou, Shi Xue ^a  

^a UNIVERSITY OF WOLLONGONG   (Australia)

^b The British University in Egypt   (Egypt)

Author keywords

Fading Kalman filter; Open circuit voltage; Real time estimation; Recursive least squares; State of charge

Indexed keywords

BANDPASS FILTERS; BATTERY ELECTRIC VEHICLES; CHARGING (BATTERIES); DYNAMOMETERS; ELECTRIC BATTERIES; ERRORS; ESTIMATION; GENETIC ALGORITHMS; KALMAN FILTERS; OPEN CIRCUIT VOLTAGE; RECONFIGURABLE HARDWARE; SECONDARY BATTERIES;

BATTERY-POWERED ELECTRIC VEHICLES; LARGE ESTIMATION ERRORS; LITHIUM IRON PHOSPHATES; REAL-TIME ESTIMATION; RECURSIVE LEAST SQUARE (RLS); STATE OF CHARGE; STATE-OF-CHARGE ESTIMATION; URBAN DYNAMOMETER DRIVING SCHEDULES;

BATTERY MANAGEMENT SYSTEMS;

ELECTRICAL POWER; ERROR ANALYSIS; ESTIMATION METHOD; IRON; KALMAN FILTER; MODEL VALIDATION; PHOSPHATE;

EID: 84958149633     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.01.096     Document Type: Article

References (33)

1. Jugovic D., Uskokovic D. A review of recent developments in the synthesis procedures of lithium iron phosphate powders. J Power Sources 2009, 190(2):538-544.
2. Roscher M., Assfalg J., Bohlen O. Detection of utilizable capacity deterioration in battery systems. IEEE Trans Vehic Technol 2011, 60(1):98-103.
3. Goodenough JB, Padhi AK, Nanjundaswamy KS, Masquelier C. Cathode materials for secondary (rechargeable) lithium batteries. Patent US6514640; 2003.
4. 4 battery using invariant imbedding method. Appl Energy 2016, 162:163-171.
5. Monem M.A., Trad K., Omar N., Hegazy O., Mantels B., Mulder G., et al. Lithium-ion batteries: evaluation study of different charging methodologies based on aging process. Appl Energy 2015, 152:143-155.
6. Omar N, Van den Bossche P, Mulder G, Daowd M, Timmermans J, Van Mierlo J, et al. Assessment of performance of lithium iron phosphate oxide, nickel manganese cobalt oxide and nickel cobalt aluminum oxide based cells for using in plug-in battery electric vehicle applications. In: Proc IEEE Conf Vehic Power and Propulsion; 2011. p. 1-7.
7. Ritchie A., Howard W. Recent developments and likely advances in lithium-ion batteries. J Power Sources 2006, 162(2):809-812.
8. Waag W., Fleischer C., Sauer D.U. Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J Power Sources 2014, 258:321-339.
9. Piller S., Perrin M., Jossen A. Methods for state-of-charge determination and their applications. J Power Sources 2001, 96(1):113-120.
10. Xing Y., He W., Pecht M., Tsui K.L. State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Appl Energy 2014, 113:106-115.
11. Zhang C, Li K, Mcloone S, Yang Z. Battery modelling methods for electric vehicles - a review. In: European Control Conf; 2014. p. 2673-8.
12. Sun F., Xiong R., He H. A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Appl Energy 2016, 162:1399-1409.
13. Charkhgard M., Farrokhi M. State-of-charge estimation for lithium-ion batteries using neural networks and EKF. IEEE Trans Ind Electron 2010, 57(12):4178-4187.
14. Rael S, Urbain M, Renaudineau H. A mathematical lithium-ion battery model implemented in an electrical engineering simulation software. In: Proc IEEE Int Symp Ind Electron; 2014. p. 1760-5.
15. Prez G., Garmendia M., Reynaud J.F., Crego J., Viscarret U. Enhanced closed loop state of charge estimator for lithium-ion batteries based on extended Kalman filter. Appl Energy 2015, 155:834-845.
16. Plett G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 1. Background. J Power Sources 2004, 134(2):252-261.
17. Wang Y., Zhang C., Chen Z. On-line battery state-of-charge estimation based on an integrated estimator. Appl Energy 2015, 10.1016/j.apenergy.2015.09.015.
18. Plett G.L. Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 1: introduction and state estimation. J Power Sources 2006, 161(2):1356-1368.
19. He H., Xiong R., Peng J. Real-time estimation of battery state-of-charge with unscented Kalman filter and RTOS μCOS-II platform. Appl Energy 2016, 162:1410-1418.
20. Sun F., Hu X., Zou Y., Li S. Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles. Energy 2011, 36(5):3531-3540.
21. Kim I.-S. Nonlinear state of charge estimator for hybrid electric vehicle battery. IEEE Trans Power Electron 2008, 23(4):2027-2034.
22. Xia Q, Rao M, Ying Y, Shen SX. A new state estimation algorithm-adaptive fading Kalman filter. In: Proc IEEE Conf Decis Control; 1992. p. 1216-21.
23. Fagin S.L. Recursive linear regression theory, optimal filter theory and error analysis of optimal systems. IRE Conv Rec 1964, 216-240.
24. Gholizadeh M., Salmasi F. Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model. IEEE Trans Ind Electron 2014, 61(3):1335-1344.
25. Huria T, Ceraolo M, Gazzarri J, Jackey R. High fidelity electrical model with thermal dependence for characterization and simulation of high power lithium battery cells. In: Proc IEEE Int Conf Electr Vehic; 2012. p. 1-8.
26. Chun C.Y., Baek J., Seo G.-S., Cho B., Kim J., Chang I.K., et al. Current sensor-less state-of-charge estimation algorithm for lithium-ion batteries utilizing filtered terminal voltage. J Power Sources 2015, 273(0):255-263.
27. 4 batteries. IEEE Trans Vehic Technol 2014, 63(8):3592-3599.
28. Oppenheim A.V., Schafer R.W. Discrete time signal processing 2010, Pearson Higher Educ., Inc., Upper Saddle River, NJ, USA. 3rd ed.
29. Perea L, How J, Breger L, Elosegui P. Nonlinearity in sensor fusion: divergence issues in EKF, modified truncated GSF, and UKF. In: AIAA Guidance, Navig Control Conf Exhibit; 2007 [AIAA-2007-6514].
30. Fitzgerald R.J. Divergence of the Kalman filter. IEEE Trans Autom Control 1971, 16(6):736-747.
31. Toth R. Modeling and identification of linear parameter-varying systems 2010, vol. 403. Springer, Germany.
32. 4 batteries in electric vehicles. Appl Energy 2012, 89(1):413-420.
33. 4-based lithium ion secondary batteries. J Power Sources 2011, 196(1):331-336.