A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm

Applied Energy

Volumn 166, Issue , 2016, Pages 76-83

  Lin, Cheng ^a   Mu, Hao ^a   Xiong, Rui ^a   Shen, Weixiang ^b  

^a BEIJING INSTITUTE OF TECHNOLOGY   (China)

^b SWINBURNE UNIVERSITY OF TECHNOLOGY   (Australia)

Author keywords

Batteries; Electric vehicles; H infinity; Multi model probability; State of charge estimation

Indexed keywords

ALGORITHMS; BATTERY ELECTRIC VEHICLES; BATTERY MANAGEMENT SYSTEMS; CHARGING (BATTERIES); ELECTRIC BATTERIES; ELECTRIC VEHICLES; GENETIC ALGORITHMS; LINEAR MATRIX INEQUALITIES; LITHIUM ALLOYS; LITHIUM COMPOUNDS; LITHIUM-ION BATTERIES; PROBABILITY; SOLAR CELLS;

APPLICATION PERFORMANCE; BATTERY STATE OF CHARGE; DISCHARGING PERIODS; EQUIVALENT CIRCUIT MODEL; ESTIMATION APPROACHES; H-INFINITY; MULTI MODEL; STATE-OF-CHARGE ESTIMATION;

SECONDARY BATTERIES;

ACCURACY ASSESSMENT; ELECTRIC FIELD; ELECTRIC VEHICLE; ESTIMATION METHOD; FEASIBILITY STUDY; GENETIC ALGORITHM; LITHIUM; POLARIZATION; PROBABILITY;

EID: 84955304218     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2016.01.010     Document Type: Article

References (40)

1. Thackeray M.M., Wolverton C., Isaacs E.D. Electrical energy storage for transportation-approaching the limits of, and going beyond, lithium-ion batteries. Energy Environ Sci 2012, 5:7854-7863.
2. Cuma M.U., Koroglu T. A comprehensive review on estimation strategies used in hybrid and battery electric vehicles. Renew Sustain Energy Rev 2015, 42:517-531.
3. Fleischer C., Waag W., Heyn H., Sauer D. 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 2014, 260:276-291.
4. Wang Y., Zhang C., Chen Z. A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy. Appl Energy 2015, 137:427-434.
5. Dubarry M., Svoboda V., Hwu R., Liaw B. Capacity loss in rechargeable lithium cells during cycle life testing: the importance of determining state-of-charge. J Power Sources 2007, 174:1121-1125.
6. Einhorn M., Conte F., Kral C., Fleig J. A method for online capacity estimation of lithium ion battery cells using the state of charge and the transferred charge. IEEE Trans Ind Appl 2012, 48:734-741.
7. Waag W., Sauer D. Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination. Appl Energy 2013, 111:416-427.
8. Cuadras A, Kanoun O. SoC Li-ion battery monitoring with impedance spectroscopy. In: International multi-conference on systems, signals and devices; 2009. p. 1-6.
9. KongSoon N., Chin-Sien M., Yi-Ping C., Yao-Ching H. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl Energy 2009, 86:1506-1511.
10. 4 battery by a revised Ah counting. Electrochim Acta 2015, 151:63-71.
11. Plett G. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs-Part 3. State and parameter estimation. J Power Sources 2004, 134:277-292.
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. Plett G. 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.
14. Dai H., Wei X., Sun Z., Wang J., Gu W. Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications. Appl Energy 2012, 95:227-237.
15. 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.
16. Tian Y., Xia B., Sun W., Xu Z., Zheng W. A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter. J Power Sources 2014, 270:619-626.
17. Hu Y., Yurkovich S. Battery cell state-of-charge estimation using linear parameter varying system techniques. J Power Sources 2012, 198:338-350.
18. Zhang F, Liu G, Fang L. A battery state of charge estimation method using sliding mode observer. In: 7th world congress on intelligent control and automation WCICA; 2008. p. 989-94.
19. Zhang F., Liu G., Fang L., Wang H. Estimation of battery state of charge with H∞ observer: applied to a robot for inspecting power transmission lines. IEEE Trans Ind Electron 2012, 59(2):1086-1095.
20. Xu J., Mi C., Cao B., Deng J., Chen Z., Li S. The state of charge estimation of lithium-ion batteries based on a proportional-integral observer. IEEE Trans Veh Technol 2014, 63:1614-1621.
21. Xia B., Chen C., Tian Y., Sun W., Xu Z., Zheng W. A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer. J Power Sources 2014, 270:359-366.
22. Chen X., Shen W., Cao Z., Kapoor A. A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles. J Power Sources 2014, 246:667-678.
23. Zhou F, Wang L, Lin H, Lv Z. High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on adaptive wavelet neural network. In: IEEE ECCE Asia DownUnder (ECCE Asia); 2013. p. 513-7.
24. Du J., Liu Z., Wang Y. State of charge estimation for Li-ion battery based on model from extreme learning machine. Control Eng Practice 2014, 26:11-19.
25. Dai H., Guo P., Wei X., Sun Z., Wang J. ANFIS (adaptive neuro-fuzzy inference system) based online SOC (State of Charge) correction considering cell divergence for the EV (electric vehicle) traction batteries. Energy 2015, 80:350-360.
26. Anton J., Nieto P., Viejo C., Vilan J. Support vector machines used to estimate the battery state of charge. IEEE Trans Power Electron 2013, 28(12):5919-5926.
27. Sheng H., Xiao J. Electric vehicle state of charge estimation: nonlinear correlation and fuzzy support vector machine. J Power Sources 2015, 281:131-137.
28. Barré A., Deguilhem B., Grolleau S., et al. A review on lithium-ion battery ageing mechanisms and estimations for automotive applications. J Power Sources 2013, 241:680-689.
29. Ouyang M., Liu G., Lu L., Li J., Han X. Enhancing the estimation accuracy in low state-of-charge area: a novel onboard battery model through surface state of charge determination. J Power Sources 2014, 270:221-237.
30. Xiong R., Sun F., He H. A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion polymer battery in electric vehicles. Appl Energy 2014, 113:463-476.
31. Li J., Barillas J.K., Guenther C., Danzer M.A. A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles. J Power Sources 2013, 230:244-250.
32. He H., Xiong R., Fan J. Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies 2011, 4:582-598.
33. Zhou K., Doyle J. Essentials of robust control 1998, Prentice-Hall, Englewood Cliffs, NJ.
34. Li H., Fu M. A linear matrix inequality approach to robust H∞ filtering. IEEE Trans Signal Process 1997, 45(9):2338-2350.
35. Li X.R., Zhu Y., Wang J., Han C. Optimal linear estimation fusion Part I: unified fusion rules. IEEE Trans Inf Theory 2003, 49(9):2192-2208.
36. Li W., Jia Y., Du J., Zhang J. Distributed multiple-model estimation for simultaneous localization and tracking with NLOS mitigation. IEEE Trans Veh Technol 2013, 62(6):2824-2830.
37. Qi C., Varshney P., Michels J., Belcastro C. Distributed fault detection with correlated decision fusion. IEEE Trans Aerospace Electron Syst 2009, 45(4):1448-1465.
38. Avvari G.V., Balasingam B., Pattipati K.R., Bar-Shalom Y. A battery chemistry-adaptive fuel gauge using probabilistic data association. J Power Sources 2015, 273:185-195.
39. Simon D. Kalman filter generalizations, in optimal state estimation: kalman, and nonlinear approaches 2006, John Wiley & Sons Inc., Hoboken, NJ, USA.
40. Sun F., Xiong R. A novel dual-scale cell state-of-charge estimation approach for series-connected battery pack used in electric vehicles. J Power Sources 2015, 274:582-594.