Abstract
Estimation of the state of charge (SOC) of a lithium-ion battery is one of the key technologies in battery management systems. The accuracy of SOC estimation mainly depends on the accuracy of the battery model. The traditional Thevenin model has limited application due to its fixed parameters. In addition, its accuracy is not high. This paper proposes a variable parameter equivalent hysteresis model based on the Thevenin model. The parameters of this model are regarded as variables that vary with temperature and SOC. They can be identified by hybrid pulse power characteristic (HPPC) experiments. In addition, the model also considers the hysteresis characteristics of the open circuit voltage (OCV) and uses a mathematical recursive equation to describe it. Experimental and simulation results show that the proposed model has a higher accuracy and a wider application than the Thevenin model. On the basis of this model, SOC estimation is carried out based on modified covariance extended Kalman filter (MVEKF) at different temperatures. The results show that the SOC estimation accuracy of the MVEKF method is significantly higher than that of an extended Kalman filter (EKF).
Similar content being viewed by others
References
Cheng, M., Tong, M.H.: Development status and trend of electric vehicles in China. Chin. J. Electr. Eng. 3(2), 1–13 (2017)
Liu, K.L., Kang, L.I., Peng, Q., Zhang, C.: A brief review on key technologies in the battery management system of electric vehicles. Front. Mech. Eng. 14(1), 47–64 (2018)
Wang, Y.S., Yang, S.Z., You, Y.: High-capacity and long-cycle life aqueous rechargeable lithium-ion battery with the FePO4 anode. ACS Appl. Mater. Interfaces. 10(8), 7061–7068 (2018)
Jiao, X.X., Liu, Y.Y., Li, B., Zhang, W.X.: Amorphous phosphorus-carbon nanotube hybrid anode with ultralong cycle life and high-rate capability for lithium-ion battery. Carbon 148, 518–524 (2019)
Yang, F.F., Xing, Y.J., Wang, D.: A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile. Appl. Energy 164, 387–399 (2016)
He, Y., Zhang, C.B., Liu, X.T., Chen, Z.H.: SOC estimation for LiFePO4 high-power batteries based on information fusion. Control Decision 29(01), 188–192 (2014)
Deng, Z.W., Hu, X.S., Lin, X.K., Che, Y.H., Guo, W.C.: Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression. Energy 205, 118000 (2020)
Zhang, X., Wang, X., Zhang, W., Lei, G.: A simplified li-ion battery SOC estimating method. Trans. Electr. Electron. Mater. 17(1), 13–17 (2016)
Eziani, S., Ouassaid, M.:State of charge estimation of supercapacitor using artificial neural network for onboard railway applications. In: International Renewable and Sustainable Energy Conference (IRSEC). (2019)
Misyris, G.S., Doukas, D.I., Papadopoulos, T.A.: State-of-charge estimation for li-ion batteries: a more accurate hybrid approach. IEEE Trans. Energy. Convers. 34(1), 109–119 (2019)
Xi, Z.M., Dahmardeh, M., Xia, B.: Learning of battery model bias for effective state of charge estimation of lithium-ion batteries. IEEE Trans. Veh. Technol. 68(9), 8613–8628 (2019)
Li, Y., Xiong, B. Y., Vilathgamuwa, D. M., Wei, Z. B., Zou, C. F.: Constrained ensemble Kalman filter for distributed electrochemical state estimation of lithium-ion batteries. IEEE Trans. Ind. Informat. PP(99) (2020)
Sturm, J., Ennifar, H., Erhard, S.V., Rheinfeld, A., Kosch, S., Jossen, A.: State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter. Appl. Energy 223, 103–123 (2018)
Susanna, S., Dewangga, B. R., Wahyungoro, O., Cahyadi, A. I.: Comparison of simple battery model and thevenin battery model for SOC estimation based on OCV method. In: International Conference on Information and Communications Technology (ICOIACT), pp. 738–743 (2019)
Liu, D., Wang, X. C., Zhang, M., Gong, M. X.: SOC estimation of lithium battery based on N-2RC model in electric vehicle. In: Chinese Control And Decision Conference (CCDC), pp. 2916–2921 (2019)
Du, J., Wang, Y. Y., Wen, C. Y.: Li-ion battery SOC estimation using particle filter based on an equivalent circuit model. In: IEEE International Conference on Control and Automation (IEEE ICCA), pp. 580–585 (2013)
Zhang, L., Wang, S.L., Stroe, D.I., Zou, C.Y., Fernandez, C.: An accurate time constant parameter determination method for the varying condition equivalent circuit model of lithium batteries. Energies 13(8), 2057 (2020)
Luo, M.J., Guo, Y.Z., Kang, J.Q., She, L.Y.: Ternary-material lithium-ion battery SOC estimation under various ambient temperature. Ionics 24(7), 1907–1917 (2018)
Zhu, J.G., Knapp, M., Darma, M.S.D., Fang, Q.H., Wang, X.Y.: An improved electro-thermal battery model complemented by current dependent para-meters for vehicular low temperature application. Appl. Energy 248, 149–161 (2019)
He, Y., Cao, C.Y., Liu, X.T., Zheng, X.X., Zeng, G.J.: SOC estimation of lithium battery based on variable temperature model. Electr. Machines Control 22(01), 43–52 (2018)
Liu, X.T., Li, H., He, Y., Zheng, X.X., Zeng, G.J.: SOC estimation method based on IUPF algorithm and variable para-meter battery model. J. Southeast Univ. (Natural Science Edition) 48(01), 54–62 (2018)
Li, Y.W., Wang, C., Gong, J.F.: A wavelet transform-adaptive unscented Kalman filter approach for state of charge estimation of LiFePo4 battery. Int. J. Energy Res. 42(2), 587–600 (2018)
Chin, C.S., Gao, Z.C., Chiew, J.H.K., Zhang, C.Z.: Nonlinear temperature-dependent state model of cylindrical LiFePO4 battery for open-circuit voltage, terminal voltage and state-of-charge estimation with extended Kalman filter. Energies 11(9), 2467 (2018)
Tan, X. J.: Design of electric vehicle power battery management system. Sun Yatsen University press (2011)
Deng, Z.W., Yang, L., Cai, Y.S., Deng, H., Sun, L.: Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery. Energy 112, 469–480 (2016)
National Automobile Standardization Technical Committee.: QC/T743—2006 Lithium ion batteries for electric vehicles. Beijing: Standards Press of China (2006)
Liu, X.T., He, Y., Zeng, G.J., Zhang, J.F., Zheng, X.X.: A method for state-of-power estimation of li-ion battery considering battery surface temperature. Energy Technol. 6(7), 1352–1360 (2018)
Liu, X.T., Chen, Z.H., Zhang, C.B., He, Y.: A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation. Appl. Energy 123, 263–272 (2014)
Zhang, R.F., Xia, B.Z., Li, B.H., Cao, L.B., Lai, Y.Z., Zheng, W.W.: A study on the open circuit voltage and state of charge characterization of high capacity lithium-ion battery under different temperature. Energies 11(9), 2408 (2018)
Chen, Y.J., Yang, G., Liu, X., He, Z.C.: A time-efficient and accurate open circuit voltage estimation method for lithium-ion batteries. Energies 12(9), 1803 (2019)
Heo, S., Park, C.G.: Consistent EKF-based visual-inertial odometry on matrix lie group. IEEE Sens. 18(9), 3780–3788 (2018)
Xie, J.L., Ma, J.C., Chen, J.: Peukert-equation-based state-of-charge estimation for LiFePO4 batteries considering the battery thermal evolution effect. Energies 11(5), 1112 (2018)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
He, Y., Li, Q., Zheng, X. et al. Equivalent hysteresis model based SOC estimation with variable parameters considering temperature. J. Power Electron. 21, 590–602 (2021). https://doi.org/10.1007/s43236-020-00213-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s43236-020-00213-5