- Volume 16 Issue 3
DOI QR Code
A Novel Sliding Mode Observer for State of Charge Estimation of EV Lithium Batteries
Chen, Qiaoyan;Jiang, Jiuchun;Liu, Sijia;Zhang, Caiping
- Received : 2015.07.22
- Accepted : 2015.12.01
- Published : 2016.05.20
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.
Lithium battery;Sliding mode observer;State estimation;State of charge
- 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. https://doi.org/10.1016/j.apenergy.2008.11.021
- 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. https://doi.org/10.1109/41.161471
- 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. https://doi.org/10.1016/j.enconman.2008.03.013
- 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. https://doi.org/10.1109/TIE.2010.2043035
- 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. https://doi.org/10.1016/j.apenergy.2014.01.066
- 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. https://doi.org/10.1007/s12239-008-0090-x
- 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. https://doi.org/10.1016/j.jpowsour.2004.02.033
- 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. https://doi.org/10.1016/j.jpowsour.2006.06.003
- 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. https://doi.org/10.1016/j.jpowsour.2006.06.004
- 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.
- 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.
- 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. https://doi.org/10.1016/j.apenergy.2013.05.001
- 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.
- 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. https://doi.org/10.1007/s12239-008-0073-y
- 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. https://doi.org/10.1016/j.jpowsour.2006.09.006
- 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.
- 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. https://doi.org/10.1109/TPEL.2009.2034966
- 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. https://doi.org/10.1109/TPEL.2008.924629
- 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.
- 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. https://doi.org/10.3390/en6105538
- 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. https://doi.org/10.1016/j.jpowsour.2014.06.152
- 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.
- 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.
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