Abstract
SOC estimation is currently a function of the energy management system for new energy vehicles. Based on the SOC of batteries, the remaining available capacity of batteries can be directly determined to determine the remaining driving range of electric vehicles. Aiming at this problem, this paper use the two Resistance and Capacitance equivalent circuit model for the ternary lithium-ion battery, and then obtains the OCV-SOC curve by using spline interpolation. The improved recursive least squares (FFRLS) method with forgetting factor is used to identify parameters of the battery model. Due to the nonlinear state of the external characteristics of the battery, the linear kalman filter would lead to a large error in the estimation, which cannot meet the need for accuracy. Therefore, in this paper, EKF is improved in this paper, and spline interpolation is used to optimize the relationship between open circuit voltage and SOC in data processing, thus improving the estimation accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Lian, B., Adam, S., Li, X., Yu, D., Wang, C., Dunn, R.W.: Optimizing LiFePO4 battery energy storage systems for frequency response in the UK system. IEEE Trans. Sustainable Energy, 8(1), 385–394 (2017)
Chemali, E., Kollmeyer, P.J., Preindl, M., Ahmed, R., Emadi, A.: Long short-term memory-networks for accurate state of charge estimation of li-ion batteries. IEEE Trans. Ind. Electron. 65(8), 6730–6739 (2018)
Xiong, R., Cao, J., Yu, Q., He, H., Sun, F.: Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access, 6, 1832–1843 (2017)
Meng, J., et al.: An overview and comparison of online implementable SOC estimation methods for Lithium-Ion battery. IEEE Trans. Ind. Appl. 54(2), 1583–1591 (2018)
Wang, W., Wang, X., Xiang, C., Wei, C., Zhao, Y.: Unscented kalman filter-based battery SOC estimation and peak power prediction method for power distribution of hybrid electric vehicles. IEEE Access, 6, 35957–35965 (2018)
El Din, M.S., Hussein, A.A., Abdel-Hafez, M.F.: Improved battery SOC estimation accuracy using a modified UKF with an adaptive cell model under real EV operating conditions. IEEE Trans. Transport. Electrification, 4(2), 408–417 (2018)
Rahimi-Eichi, H., Baronti, F., Chow, M.-Y.: Online adaptive parameter identification and state-of-charge coestimation for lithium-polymer battery cells. IEEE Trans. Ind. Electron. 64(4), 2053–2061 (2014)
Meng, J., Stroe, D.J., Ricco, M., Luo, G., Teodorescu, R.: A simplified model based state-of-charge estimation approach for lithium-ion battery with dynamic linear model. IEEE Trans. Ind. Electron. 66(10), 7717–7727 (2016)
Acknowledgment
This work was financially supported by the Heilongjiang Provincial Colleges and Universities Basic Scientific Research Business Expense Project(Hkdqg201908).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, D., Yang, Y., Zhao, W., Yu, T., Zhang, D. (2020). SOC Estimation of Ternary Lithium Battery Based on Interpolation Method and Online Parameter Identification. In: Jiang, X., Li, P. (eds) Green Energy and Networking. GreeNets 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 333. Springer, Cham. https://doi.org/10.1007/978-3-030-62483-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-62483-5_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62482-8
Online ISBN: 978-3-030-62483-5
eBook Packages: Computer ScienceComputer Science (R0)