Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares

Journal of Power Sources

Volumn 296, Issue , 2015, Pages 215-224

  Duong, Van Huan ^a   Bastawrous, Hany Ayad ^a,b   Lim, KaiChin ^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

Battery management system; LiFePO4 battery; Model parameters estimation; Multiple adaptive forgetting factors; Recursive least squares estimation; State of charge estimation

Indexed keywords

CHARGING (BATTERIES); DYNAMOMETERS; ELECTRIC VEHICLES; IRON COMPOUNDS; LITHIUM COMPOUNDS; LITHIUM-ION BATTERIES; OPEN CIRCUIT VOLTAGE; PARAMETER ESTIMATION;

FORGETTING FACTORS; LIFEPO4; MODEL PARAMETERS ESTIMATION; RECURSIVE LEAST-SQUARES ESTIMATION; STATE-OF-CHARGE ESTIMATION;

BATTERY MANAGEMENT SYSTEMS;

EID: 84937916952     PISSN: 03787753     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jpowsour.2015.07.041     Document Type: Article

References (41)

1. K. Zaghib, K. Striebel, A. Guerfi, J. Shim, M. Armand, and M. Gauthier LiFePO4/polymer/natural graphite: low cost Li-ion batteries Electrochim. Acta 50 2-3 2004 263 270
2. C. Mi, G. Cao, and X. Zhao Low-cost, one-step process for synthesis of carbon-coated LiFePO4 cathode Mater. Lett. 59 1 2005 127 130
3. K. Striebel, J. Shim, A. Sierra, H. Yang, X. Song, R. Kostecki, and K. McCarthy The development of low cost LiFePO4-based high power lithium-ion batteries J. Power Sources 146 1-2 2005 33 38
4. Y. Zheng, M. Ouyang, L. Lu, J. Li, X. Han, L. Xu, H. Ma, T.A. Dollmeyer, and V. Freyermuth Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model Appl. Energy 111 0 2013 571 580
5. W. Waag, C. Fleischer, and D.U. Sauer Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles J. Power Sources 258 0 2014 321 339
6. C. Fleischer, W. Waag, H.-M. Heyn, and D.U. Sauer On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models: part 1. requirements, critical review of methods and modeling J. Power Sources 260 2014 276 291
7. H. Rahimi-Eichi, U. Ojha, F. Baronti, and M. Chow Battery management system: an overview of its application in the smart grid and electric vehicles Ind. Electron. Mag. IEEE 7 2 2013 4 16
8. L. Lu, X. Han, J. Li, J. Hua, and M. Ouyang A review on the key issues for lithium-ion battery management in electric vehicles J. Power Sources 226 0 2013 272 288
9. L. Lam, P. Bauer, and E. Kelder A practical circuit-based model for li-ion battery cells in electric vehicle applications Telecommunications Energy Conference (INTELEC), 2011 IEEE 33rd International 2011 1 9
10. M. Chen, and G. Rincon-Mora Accurate electrical battery model capable of predicting runtime and I-V performance Energy Convers. IEEE Trans. 21 2 2006 504 511
11. G.L. Plett Extended Kalman filtering for battery management systems of LiPB-based {HEV} battery packs: part 1. background J. Power Sources 134 2 2004 252 261
12. 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 2 2004 277 292
13. J. Lee, O. Nam, and B. Cho Li-ion battery {SOC} estimation method based on the reduced order extended kalman filtering J. Power Sources 174 1 2007 9 15
14. R. Xiong, H. He, F. Sun, and K. Zhao Evaluation on state of charge estimation of batteries with adaptive extended Kalman filter by experiment approach Veh. Technol. IEEE Trans. 62 1 2013 108 117
15. ∞ observer: applied to a robot for inspecting power transmission lines Ind. Electron. IEEE Trans. 59 2 2012 1086 1095
16. A. Alfi, M. Charkhgard, and M. Zarif Hybrid state of charge estimation for lithium-ion batteries: design and implementation Power Electron. IET 7 11 2014 2758 2764
17. H. He, X. Zhang, R. Xiong, Y. Xu, and H. Guo Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles Energy 39 1 2012 310 318
18. W. Waag, S. Käbitz, and D.U. Sauer Experimental investigation of the lithium-ion battery impedance characteristic at various conditions and aging states and its influence on the application Appl. Energy 102 0 2013 885 897
19. 4 batteries in electric vehicles Appl. Energy 89 1 2012 413 420
20. H. Rahimi-Eichi, F. Baronti, and M.-Y. Chow Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells Industrial Electron. IEEE Trans. 61 4 2014 2053 2061
21. M. Roscher, O. Bohlen, and D. Sauer Reliable state estimation of multicell lithium-ion battery systems Energy Convers. IEEE Trans. 26 3 2011 737 743
22. R. Xiong, F. Sun, X. Gong, and C. Gao A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles Appl. Energy 113 0 2014 1421 1433
23. A. Vahidi, A. Stefanopoulou, and H. Peng Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments Veh. Syst. Dyn. 43 1 2005 31 55
24. C. Paleologu, J. Benesty, and S. Ciochina A robust variable forgetting factor recursive least-squares algorithm for system identification Signal Process. Lett. IEEE 15 2008 597 600
25. M.A. Roscher, and D.U. Sauer Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries J. Power Sources 196 1 2011 331 336
26. V.-H. Duong, N.-T. Tran, Y.-J. Park, and W. Choi Novel estimation technique for the state-of-charge of the lead-acid battery by using EKF considering diffusion and hysteresis phenomenon Trans. Korean Inst. Power Electron. 19 2 2014 139 148
27. W.S. Levine The Control Handbook, the Electrical Engineering Handbook Series 1996 CRC Press New York, Boca Raton (Fl.) 265 271
28. K.J. Åström, and B. Wittenmark Adaptive Control 2013 Courier Corporation 52 55
29. T. Fortescue, L. Kershenbaum, and B. Ydstie Implementation of self-tuning regulators with variable forgetting factors Automatica 17 6 1981 831 835
30. B.D. Ketelaere, H. Saelens, L. Wulteputte, and J. Anthonis Nozzle fault detection using recursive least-squares 20th Annu. Conf. Liquid Atomization and Spray Syst., Chicago, USA 2007
31. S. Saelid, and B. Foss Adaptive controllers with a vector variable forgetting factor Decision and Control, 1983. The 22nd IEEE Conference on 1983 1488 1494
32. J. Parkum, N. Poulsen, and J. Holst Selective Forgetting in Adaptive Procedures 1990 180 185
33. N. Yoshitani, and A. Hasegawa Model-based control of strip temperature for the heating furnace in continuous annealing Control Syst. Technol. IEEE Trans. 6 2 1998 146 156
34. A. Albanese, N. Karamolegkos, S. Haider, A. Seiver, and N. Chbat Real-time noninvasive estimation of intrapleural pressure in mechanically ventilated patients: a feasibility study Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE 2013 5211 5215
35. M. Verbrugge Adaptive, multi-parameter battery state estimator with optimized time-weighting factors J. Appl. Electrochem. 37 5 2007 605 616
36. V.-H. Duong, H.A. Bastawrous, K. Lim, K.W. See, P. Zhang, and S.X. Dou SOC estimation for LiFePO4 battery in EVs using recursive least-squares with multiple adaptive forgetting factors Connected Vehicles and Expo (ICCVE), 2014 International Conference on 2014 516 517
37. F. Baronti, W. Zamboni, N. Femia, H. Rahimi-Eichi, R. Roncella, S. Rosi, R. Saletti, and M.-Y. Chow Parameter identification of li-po batteries in electric vehicles: a comparative study IEEE International Symposium on, 2013 2013 Industrial Electronics (ISIE) 1 7
38. Y. Gao, and M. Ehsani Design and control methodology of plug-in hybrid electric vehicles Vehicle Power and Propulsion Conference, 2008. VPPC'08 2008 IEEE 1 6
39. J. Ryu, Y. Park, and M. Sunwoo Electric powertrain modeling of a fuel cell hybrid electric vehicle and development of a power distribution algorithm based on driving mode recognition J. Power Sources 195 17 2010 5735 5748
40. B. Lindoff On the Optimal Choice of the Forgetting Factor in the Recursive Least Squares Estimator Tech. rep. 1997 Department of Mathematical Statistics, Lund Institute of Technology Lund, Sweden
41. C. Fleischer, W. Waag, H.-M. Heyn, and D.U. Sauer On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation J. Power Sources 262 0 2014 457 482