State of charge estimation for Li-ion battery based on model from extreme learning machine

Control Engineering Practice

Volumn 26, Issue 1, 2014, Pages 11-19

  Du, Jiani ^a   Liu, Zhitao ^b   Wang, Youyi ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b TUM CREATE   (Singapore)

Author keywords

Adaptive unscented Kalman filter (AUKF); Battery modeling; Extreme learning machine (ELM); State of charge (SOC) estimation

Indexed keywords

ADAPTIVE EXTENDED KALMAN FILTERS; ADAPTIVE UNSCENTED KALMAN FILTER (AUKF); BATTERY MODELING; EXTREME LEARNING MACHINE; RADIAL BASIS FUNCTION NEURAL NETWORKS; STATE ESTIMATION METHODS; STATE OF CHARGE; STATE-OF-CHARGE ESTIMATION;

ALGORITHMS; EXTENDED KALMAN FILTERS; KNOWLEDGE ACQUISITION; LEAD ACID BATTERIES; LEARNING SYSTEMS; LITHIUM; LITHIUM BATTERIES; NONLINEAR FILTERING;

ESTIMATION;

EID: 84897715956     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2013.12.014     Document Type: Article

References (41)

1. Almagbile A., Wang J., Ding W. Evaluating the performances of adaptive kalman filter methods in GPS/INS integration. Journal of Global Positioning Systems 2010, 9:33-40.
2. Barbarisi O., Vasca F., Glielmo L. State of charge Kalman filter estimator for automotive batteries. Control Engineering Practice 2006, 14:267-275.
3. Charkhgard M., Farrokhi M. State-of-charge estimation for lithium-ion batteries using neural networks and EKF. IEEE Transactions on Industrial Electronics 2010, 57:4178-4187.
4. Chaturvedi N.A., Klein R., Christensen J., Ahmed J., Kojic A. Algorithms for advanced battery managemetn systems. IEEE Control Systems Magazine 2010, 30:49-68.
5. Chen M., Rincon-Mora G.A. Accurate electrical battery model capable of predicting runtime and I-V performance. IEEE Transactions on Energy Conversion 2006, 21:504-511.
6. Chen Zheng, Fu Yuhong, Mi Chunting Chris State of charge estimation of lithium-ion batteries in electric drive vehicles using extended Kalman filter. IEEE Transactions on Vehicular Technology 2013, 62(3):1020-1030.
7. Coleman M., Chi Kwan L., Chunbo Z., 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 Transactions on Industrial Electronics 2007, 54:2550-2557.
8. de Marina H.G., Pereda F.J., Giron-Sierra J.M., Espinosa F. UAV attitude estimation using unscented Kalman filter and TRIAD. IEEE Transactions on Industrial Electronics 2012, 59:4465-4474.
9. Dinh Vinh D., Forgez C., El Kadri Benkara K., Friedrich G. Impedance observer for a Li-Ion battery using Kalman filter. IEEE Transactions on Vehicular Technology 2009, 58:3930-3937.
10. Du, J., Liu, Z., Chen, C., & Wang, Y. (2012). Li-ion battery SOC estimation using EKF based on a model proposed by extreme learning machine. In: Proceedings of the 7th IEEE conference on industrial electronics and applications (pp. 1648-1653).
11. Ehsani M., Gao Y., Emadi A. Modern electric, hybrid electric, and fuel cell vehicles: fundamentals, theory, and design 2009, CRC Press, Boca Raton, FL.
12. Faa-Jeng L., Ming-Shi H., Po-Yi Y., Han-Chang T., Chi-Hsuan K. DSP-based probabilistic fuzzy neural network control for Li-ion battery charger. IEEE Transactions on Power Electronics 2012, 27:3782-3794.
13. Fathabadi, V., Shahbazian, M., Salahshour, K., & Jargani, L. (2009). Comparison of adaptive kalman filter methods in state estimation of a nonlinear system using asynchronous measurements. In: Proceedings of world congress on engineering and computer science (pp. 1-8).
14. 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.
15. 4 batteries in electric vehicles. Applied Energy 2012, 89:413-420.
16. Hu C., Youn B.D., Chung J. A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation. Applied Energy 2012, 92:694-704.
17. Hu X., Sun F., Zou Y. Estimation of state of charge of a lithium-ion battery pack for electric vehicles using an adaptive luenberger observer. Energies 2010, 3:1586-1603.
18. Huang G.B. Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Transactions on Neural Networks 2003, 14:274-281.
19. Huang G.B., Chen L., Siew C.K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks 2006, 17:879-892.
20. Huang G.-B., Wang D.H., Lan Y. Extreme learning machines: a survey. International Journal of Machine Learning and Cybernetics 2011, 2:107-122.
21. Huang G.B., Zhu Q.Y., Mao K., Siew C.K., Saratchandran P., Sundararajan N. Can threshold networks be trained directly?. IEEE Transactions on Circuits and Systems II: Express Briefs 2006, 53:187-191.
22. Huang G.B., Zhu Q.Y., Siew C.K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70:489-501.
23. Jossen A., Späth V., Döring H., Garche J. Reliable battery operation-a challenge for the abttery management system. Journal of Power Sources 1999, 84:283-286.
24. Julier S.J., Uhlmann J.K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE 2004, 92:401-422.
25. Lee J., Nam O., Cho B. Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering. Journal of Power Sources 2007, 174:9-15.
26. Lee S., Kim J., Lee J., Cho B. State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge. Journal of Power Sources 2008, 185:1367-1373.
27. Lerro D., Bar-Shalom Y. Tracking with debiased consistent converted measurements versus EKF. IEEE Transactions on Aerospace and Electronic Systems 1993, 29:1015-1022.
28. Liang N.Y., Huang G.B., Saratchandran P., Sundararajan N. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks 2006, 17:1411-1423.
29. Liu, Z., Wang, Y., Du, J., & Chen, C. (2012). RBF network-aided adaptive unsecented Kalman filter for lithium-ion battery SOC estimation in electric vehicles. In: Proceedings of the 7th IEEE conference on industrial electronics and applications (pp. 1670-1674).
30. Mehra R. On the identification of variances and adaptive Kalman filtering. IEEE Transactions on Automatic Control 1970, 15:175-184.
31. Mohamed A., Schwarz K. Adaptive Kalman filtering for INS/GPS. Journal of Geodesy 1999, 73:193-203.
32. Piller S., Perrin M., Jossen A. Methods for state-of-charge determination and their applications. Journal of Power Sources 2001, 96:113-120.
33. Plett G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modelling and identification. Journal of Power Sources 2004, 134:262-276.
34. Plett G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Background. Journal of Power Sources 2004, 134:252-261.
35. Plett G.L. Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. Journal of Power Sources 2004, 134:277-292.
36. Schweighofer B., Raab K.M., Brasseur G. Modeling of high power automotive batteries by the use of an automated test system. IEEE Transactions on Instrumentation and Measurement 2003, 52:1087-1091.
37. Serre D. Matrices: Theory and applications 2010, Springer-Verlag, New York, NY.
38. Simon D. Optimal state estimation: Kalman, H [infinity] and nonlinear approaches 2006, John Wiley and Sons, Hoboken. NJ.
39. Smith K.A., Rahn C.D., Wang C.Y. Model-based electrochemical estimation and constraint management for pulse operation of lithium ion batteries. IEEE Transactions on Control Systems Technology 2010, 18:654-663.
40. Zenati, A., Desprez, P., & Razik H. (2010). Estimation of the SOC and the SOH of li-ion batteries, by combining impedance measurements with the fuzzy logic inference. In: Proceedings of the IECON 2010 - 36th annual conference on IEEE industrial electronics society (pp. 1773-1778).
41. Zhiwei, H., Mingyu, G., & Jie, X. (2009). EKF-Ah based state of charge online estimation for lithium-ion power battery. In: Proceedings of CIS '09 international conference on computational intelligence and security (pp. 142-145).