Skip to main content
SpringerLink
Log in

SOC Estimation of Li-Ion Power Battery Based on Strong Tracking UKF with Multiple Suboptimal Fading Factors

  • Engine and Emissions, Fuels and Lubricants, Vehicle Dynamics and Control
  • Published:
International Journal of Automotive Technology Aims and scope Submit manuscript

Abstract

A method based on strong tracking unscented Kalman filter with multiple suboptimal fading factors (MSTUKF) was proposed to accurately estimate the state of charge (SOC) of power batteries of electric vehicles online. Taking a certain lithium-ion battery as the research object, a second-order RC equivalent circuit model of the battery was established based on its external characteristics and related mechanism. Then the recursive least squares method with forgetting factor was adopted to identify the model parameters, and the MSTUKF nonlinear state space equation of the battery was established according to the equivalent circuit model. Finally, the SOC estimation algorithm was verified by simulation experiments under ECE15 and UDDS conditions. The results show that the error of MSTUKF in SOC estimation of lithium-ion battery is kept within 1.5%, so this method can estimate battery SOC accurately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All relevant data are within the paper.

References

  • Chen, Z., Wang, Z. D., Mou, W. B., Zhu, P. W., & Xiao, G. (2023). State-of-charge estimation of lead-carbon batteries based on the PNGV model and an adaptive Kalman filter algorithm. Energy Storage Science and Technology, 12(3), 941–950.

    Google Scholar 

  • Fu, S. Y., Lyu, T. L., Min, F. Q., Luo, W. L., Luo, C. D., Wu, L., & Xie, J. Y. (2021). Review of estimation methods on SOC of lithium-ion batteries in electric vehicles. Energy Storage Science and Technology, 10(3), 1127–1136.

    Google Scholar 

  • Gao, W. Z., & Huang, T. (2020). Research on SOC estimation method of unscented Kalman filter for lithium battery. Telecom Power Technology, 37(3), 19–20.

    Google Scholar 

  • Gong, M. H., Wu, J., & Jiao, C. Y. (2020). SOC estimation method of lithium battery based on fuzzy adaptive extended Kalman filter. Transactions of China Electrotechnical Society, 35(18), 3972–3978.

    Google Scholar 

  • Huang, J. Y., Li, L. F., Zhang, Y., & Song, X. Y. (2021). Estimation of state of charge for lithium-ion battery based on multi-innovation recursive least square algorithm and unscented Kalman filter. Chinese Journal of Power Sources, 45(6), 711–715.

    Google Scholar 

  • Huang, Z. J., Chen, Y., & Zhou, M. F. (2023). Soc estimation of Li-ion battery based on adaptive CKF algorithm. Chiang Mai Journal of Science, 50(6), 1–9.

    Article  Google Scholar 

  • Huang, Z. J., & Fang, Y. S. (2020). SOC estimation of Li-ion battery based on UD factorized adaptive EKF. Chinese Journal of Sensors and Actuators, 33(4), 552–556.

    MathSciNet  Google Scholar 

  • Huang, Z. J., Fang, Y. S., & Xu, J. J. (2021). SOC estimation of Li-ion battery based on improved EKF algorithm. International Journal of Automotive Technology, 22(2), 335–340.

    Article  Google Scholar 

  • Kaleli, A., & Akolas, H. I. (2023). Recursive ARMAX-based global battery SOC estimation model design using Kalman filter with optimized parameters by radial movement optimization method. Electric Power Components and Systems, 51(11), 1027–1039.

    Article  Google Scholar 

  • Kim, M.H., Kim, K.R., Kim, J.S., Yu, J.W., Han S.H. (2018). State of charge estimation for lithium ion battery based on reinforcement learning. In 10th IFAC Symposium on Control of Power and Engery Systems CPES 2018: Tokyo, Japan, vol. 51, no. 28, pp. 404–408.

  • Liu, D., Huang, B. X., Wang, Y. Q., Yan, X., & Wang, Y. (2019). Inflection point Ah-total integration method for real-time integration to correct lithium battery SOC. Energy Storage Science and Technology, 8(5), 850–855.

    Google Scholar 

  • Liu, D. L., Fan, Y. C., Wang, S. L., & Xia, L. L. (2021). Estimation of Li-ion battery SOC based on RFMRA and improved PNGV model. Battery Bimonthly, 51(5), 470–473.

    Google Scholar 

  • Liu, F., Ma, J., Su, W. X., Dou, R. Z., & Lin, H. (2020). State of charge estimation method of electric vehicle power battery life cycle based on auto regression extended Kalman filter. Transactions of China Electrotechnical Society, 35(4), 698–707.

    Google Scholar 

  • Liu, P., Li, Y. W., & Liang, X. C. (2022). Estimation of lithium battery SOC based on FFRLS and AUKF. Automobile Technology, 2, 21–27.

    Google Scholar 

  • Liu, P., Liang, X. C., & Huang, G. J. (2021). A review of lithium-ion battery models. Chinese Battery Industry, 25(2), 106–112.

    Google Scholar 

  • Qian, H. M., Huang, W., Sun, L., Xu, J. X., & Ge, L. (2013). Attitude estimation of strong tracking UKF based on multiple fading factors. Systems Engineering and Electronics, 35(3), 580–585.

    Google Scholar 

  • Shi, Y. S., Shi, L. P., Wei, H., & Yu, M. J. (2019). An improved SOC estimation method for lithium ion battery. Chinese Journal of Electron Devices, 42(1), 138–141.

    Google Scholar 

  • Sturm, J. (2018). State estimation of lithium-ion cells using a physic chemical model based extended Kalman filter. Applied Energy, 223, 103–123.

    Article  Google Scholar 

  • Wang, T. P., Chen, S. Z., & Ren, H. B. (2018). Model-based unscented Kalman filter observer design for lithium-ion battery state of charge estimation. International Journal of Energy Research, 42(4), 1603–1614.

    Article  Google Scholar 

  • Xiong, R., Cao, J. Y., & Yu, Q. Q. (2018). Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle. Applied Energy, 211, 538–548.

    Article  Google Scholar 

  • Zhou, D. H., Xi, Y. G., & Zhang, Z. J. (1991). A suboptimal multiple fading extended Kalman filter. Acta Automatica Sinica, 17(6), 689–695.

    Google Scholar 

  • Zhu, R., Duan, B., Wen, F. Z., Zhang, J. M., & Zhang, C. H. (2019). Lithium-ion battery modeling and parameter identification based on decentralized least squares method. Journal of Mechanical Engineering, 55(20), 85–93.

    Google Scholar 

Download references

Acknowledgements

This work was funded by Department of Education of Zhejiang Province, China (Project number: Y202249818).

Author information

Authors and Affiliations

  1. Mechanical and Electrical Engineering College, Jinhua Polytechnic, Jinhua, 321017, China

    Zhengjun Huang, Yu Chen & Ludan Shi

  2. Automotive Technology College, Zhejiang Technical Institute of Economics, Hangzhou, 310018, China

    Tengfei Xiang

Authors
  1. Zhengjun Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tengfei Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yu Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ludan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengjun Huang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huang, Z., Xiang, T., Chen, Y. et al. SOC Estimation of Li-Ion Power Battery Based on Strong Tracking UKF with Multiple Suboptimal Fading Factors. Int.J Automot. Technol. (2024). https://doi.org/10.1007/s12239-024-00093-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12239-024-00093-9

Keywords

Search

Navigation