[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.6113/JPE.2016.16.3.1131

A Novel Sliding Mode Observer for State of Charge Estimation of EV Lithium Batteries

Chen, Qiaoyan (National Active Distribution Network Technology Research Center (NANTEC), School of Electrical Engineering, Beijing Jiaotong University)
Jiang, Jiuchun (National Active Distribution Network Technology Research Center (NANTEC), School of Electrical Engineering, Beijing Jiaotong University)
Liu, Sijia (National Active Distribution Network Technology Research Center (NANTEC), School of Electrical Engineering, Beijing Jiaotong University)
Zhang, Caiping (National Active Distribution Network Technology Research Center (NANTEC), School of Electrical Engineering, Beijing Jiaotong University)

Publication Information

Journal of Power Electronics / v.16, no.3, 2016 , pp. 1131-1140 More about this Journal

Abstract

A simple design for a sliding mode observer is proposed for EV lithium battery SOC estimation in this paper. The proposed observer does not have the limiting conditions of existing observers. Compared to the design of previous sliding mode observers, the new observer does not require a solving matrix equation and it does not need many observers for all of the state components. As a result, it is simple in terms of calculations and convenient for engineering applications. The new observer is suitable for both time-variant and time-invariant models of battery SOC estimation, and the robustness of the new observer is proved by Liapunov stability theorem. Battery tests are performed with simulated FUDS cycles. The proposed observer is used for the SOC estimation on both unchanging parameter and changing parameter models. The estimation results show that the new observer is robust and that the estimation precision can be improved base on a more accurate battery model.

Keywords

Lithium battery; Sliding mode observer; State estimation; State of charge;

Citations & Related Records

Times Cited By KSCI : 1 (Citation Analysis)

Reference
Cited By KSCI

1	B. Pattipati, B. Balasingam, G. V. Avvari, K. R. Pattipati, and Y. Bar-Shalom, “Open circuit voltage characterization of lithium-ion batteries,” Journal of Power Sources, Vol. 269, pp. 317-333, Dec. 2014. DOI
2	S. Bao, Y. Feng, and L. Sun, “Robust sliding mode observer design of nonlinear uncertain systems,” Journal of Harbin Institute of Technology, Vol. 36, No. 5, pp. 613-616, May 2004.
3	N. Zhang, Y. Feng, and D. Qiu, “Robust sliding mode observer design of nonlinear uncertain systems,” Control Theory & Applications, Vol. 24, No. 5, pp. 715-718, Oct. 2007.
4	K. S. Ng, C. Moo, Y.-P. Chen, and Y.-C. Hsieh, “Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries,” Applied Energy, Vol. 86, No. 9, pp. 1506-1511, Sep. 2009. DOI
5	J. H, Aylor, A. Thieme, and B. W. Johnson, “A battery state of charge indicator for electric wheelchairs,” IEEE Trans. Ind. Electron., Vol. 39, No. 5, pp. 398-409, Oct. 1992. DOI
6	B. Cheng, Z. Bai, and B. Cao, “State of charge estimation based on evolutionary neural network,” Energy Conversion and Management, Vol. 49, No. 10, pp. 2788–2794, Oct. 2008. DOI
7	M. Charkhgard and M. Farrokhi, “State-of-charge estimation for lithium-ion batteries using neural networks and EKF,” IEEE Trans. Ind. Electron., Vol. 57, No. 12, pp. 4178-4187, Dec. 2010. DOI
8	L. Kang, X. Zhao, and J. Ma, “A new neural network model for the state-of-charge estimation in the battery degradation process,” Applied Energy, Vol. 121, pp. 20-27, May 2014. DOI
9	Q.-S. Shi, C.-H. Zhang, and N.-X. Cui, “Estimation of battery state-of-charge using ν-support vector regression algorithm,” International Journal of Automotive Technology, Vol. 9, No. 6, pp. 759-764, Dec. 2008. DOI
10	G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation,” Journal of Power Sources, Vol. 134, No. 2, pp. 277-292, Aug. 2004. DOI
11	G. L. Plett, “Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1. Introduction and state estimation,” Journal of Power Sources, Vol. 161, No. 2, pp. 1356-1368, Oct. 2006. DOI
12	W. Waag and D. U. Sauer, “Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination,” Applied Energy, Vol. 111, pp. 416-427, Nov. 2013. DOI
13	G. L. Plett, “Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2: Simultaneous state and parameter estimation,” Journal of Power Sources, Vol. 161, No. 2, pp. 1369-1384, Oct. 2006. DOI
14	K. Wei and Q. Chen, “States estimation of Li-ion power batteries based on adaptive unscented Kalman filters,” Proceedings of the CSEE, Vol. 34, No. 3, pp. 445-452, Jan. 2014.
15	C. Unterrieder, M. Lunglmayr, S. Marsili, and M. Huemer, "Battery state-of-charge estimation prototype using EMF voltage prediction," in IEEE International Symposium on Circuits and Systems(ISCAS), pp. 622-625, Jun. 2014.
16	C. Ehret, S. Piller, W. Schroer, and A. Jossen, "State-of-charge determination for lead-acid batteries in PV-applications," in Proceedings of the 16th European Photovoltaic Solar Energy Conference, pp. 1125-1132, 2000.
17	J. H. Jang and J. Y. Yoo, “Impedance-based and circuit-parameter-based battery models for HEV power systems,” International Journal of Automotive Technology, Vol. 9, No. 5, pp. 615-623, Oct. 2008. DOI
18	I.-S. Kim, “The novel state of charge estimation method for lithium battery using sliding mode observer,” Journal of Power Sources, Vol. 163, No. 1, pp. 584-690, Dec. 2006. DOI
19	F. Zhang, G. Liu, and L. Fang, "A battery state of charge estimation method using sliding mode observer," in Proceedings of the 7th World Congress on Intelligent Control and Automation(WCICA), pp. 989-994, Jun. 2008.
20	I. S. Kim, “A technique for estimating the state of health of lithium batteries through a dual-sliding-mode observer,” IEEE Trans. Power Electron., Vol. 25, No. 4, pp. 1013- 1022, Apr. 2010. DOI
21	I. S. Kim, “Nonlinear state of charge estimator for hybrid electric vehicle battery,” IEEE Trans. Power Electron., Vol. 23, No. 4, pp. 2027-2034, Jul. 2008. DOI
22	U. Christoph, R. Priewasser, S. Marsili, and M. Huemer, "Battery state estimation using mixed Kalman/H∞, adaptive luenberger and sliding mode observer," in IEEE Vehicle Power and Propulsion Conference (VPPC), pp. 1-6, Oct. 2013.
23	D. Kim, K. Koo, J. J. Jeong, T. Goh, and S. W. Kim, “Second-order discrete-time sliding mode observer for state of charge determination based on a dynamic resistance li-ion battery model,” Energies, Vol. 6, No. 10, pp. 5538- 5551, Oct. 2013. DOI

1	Ngoc-Tham Tran. (2017) Energies State of Charge and State of Health Estimation of AGM VRLA Batteries by Employing a Dual Extended Kalman Filter and an ARX Model for Online Parameter Estimation / 10 (1) , 137
9	(2017) Energies An Adaptive Square Root Unscented Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Batteries / 10 (9) , 1345
4	(2018) IET Electric Power Applications State of charge prediction of supercapacitors via combination of Kalman filtering and backpropagation neural network / 12 (4) , 588
1433-3058	(2018) Neural Computing and Applications Estimation algorithm research for lithium battery SOC in electric vehicles based on adaptive unscented Kalman filter / (1433-3058)