Skip to main content

Advertisement

SpringerLink
Log in

An adaptive fractional-order unscented Kalman filter for Li-ion batteries in the energy storage system

  • Original Paper
  • Published:
Indian Journal of Physics Aims and scope Submit manuscript

Abstract

Accurate estimation of the state of charge (SOC) can prolong the working life and enhance the safety of energy storage system. Considering the influence of noise and parameter changes in the operating environment, an adaptive fractional-order unscented Kalman filter algorithm is introduced to strengthen the accuracy of SOC estimation. To verify the effectiveness and robustness of the algorithm, the simulation is carried out under UDDS complex conditions. The experimental results indicate that the proposed algorithm has the highest SOC precision among the four algorithms, and the RMSE is 1.37%, indicating the superiority of the fractional-order modeling and the joint estimation algorithm. The online identification of full parameters can solve the shortcoming of the long time to obtain the open-circuit voltage in the experiment, and the adaptive filtering algorithm can overcome the problem of filtering divergence and improve the flexibility of SOC estimation.

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

Similar content being viewed by others

References

  1. T Sun, B Xia, Y Liu, Y Lai, W Zheng, H Wang, W Wang and M Wang, Energies, 12 (2019)

  2. Y Tian, D Li, J Tian and B Xia Electrochim. Acta 225 225 (2017)

    Article  Google Scholar 

  3. B Xia, R Huang, Z Lao, R Zhang, Y Lai, W Zheng, H Wang, W Wang and M Wang, Energies, 11 (2018)

  4. B Xia, Z Sun, R Zhang and Z Lao, Energies, 10 (2017)

  5. C Liu, Y Wang and Z Chen Energy 166 796 (2019)

    Article  Google Scholar 

  6. K Zhang, J Ma, X Zhao, D Zhang and Y He, IEEE Access, 7 (2019) 166657

  7. X Hu, H Yuan, C Zou, Z Li and L Zhang IEEE Trans. Veh. Technol. 67 10319 (2018)

    Article  Google Scholar 

  8. L Zhang, H Peng, Z Ning, Z Mu and C Sun Appl. Sci. 7 1002 (2017)

    Article  Google Scholar 

  9. Y Sun, Y Li, M Yu, Z Zhou, Q Zhang, B Duan, Y Shang and C Zhang, J. Power Sources, 448 (2020)

  10. M Hu, Y Li, S Li, C Fu, D Qin and Z Li Energy 165 153 (2018)

    Article  Google Scholar 

  11. G Jin, L Li, Y Xu, M Hu, C Fu and D Qin, Energies, 13 (2020)

  12. L Li, H Zhu, A Zhou, M Hu, C Fu and D Qin Int. J. Electrochem. Sci. 15 6863 (2020)

    Article  Google Scholar 

  13. S Liu, X Dong and Y Zhang IEEE. Access. 7 122949 (2019)

    Article  Google Scholar 

  14. Y Wang, G Gao, X Li and Z Chen J. Power Sources.449 227543 (2020)

    Article  Google Scholar 

  15. J Tian, R Xiong, W Shen and J Wang, Chinese Journal of Mechanical Engineering, 33 (2020)

  16. Q Zhang, Y Shang, Y Li, N Cui, B Duan and C Zhang ISA Trans 97 448 (2020)

    Article  Google Scholar 

  17. L Zhang, S Wang, D-I Stroe, C Zou, C Fernandez and C Yu Energies 13 2057 (2020)

    Article  Google Scholar 

  18. Y Xu, M Hu, A Zhou, Y Li, S Li, C Fu and C Gong Applied Mathematical Modelling 77 1255 (2020)

    Article  MathSciNet  Google Scholar 

  19. M Cai, W Chen and X Tan Energies 10 1577 (2017)

    Article  Google Scholar 

  20. J Tian, R Xiong, W Shen and J Wang Energy 176 874 (2019)

    Article  Google Scholar 

  21. Y Wang and Z Chen Appl. Energy. 260 114324 (2020)

    Article  Google Scholar 

  22. S Li, M Hu, Y Li and C Gong Int. J. Energy Res. 43 417 (2019)

    Article  Google Scholar 

  23. A N Eddine, B Huard, J-D Gabano and T Poinot Communications in Nonlinear Science and Numerical Simulation 59 375 (2018)

    Article  ADS  MathSciNet  Google Scholar 

  24. X Lai, L He, S Wang, L Zhou, Y Zhang, T Sun and Y Zheng, Journal of Cleaner Production, 255 (2020)

  25. X Lu, H Li and N Chen Electrochim. Acta 299 378 (2019)

    Article  Google Scholar 

  26. L Chen, S Wang, H Jiang, C Fernandez and X Xiong Int. J. Energy Res. 45 15481 (2021)

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported by the National Natural Science Foundation of China (No. 61801407), Sichuan science and technology program (No. 2019YFG0427), China Scholarship Council (No. 201908515099).

Author information

Authors and Affiliations

  1. School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621010, China

    L. Chen, W. Shunli & H. Jiang

  2. School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen, AB10-7GJ, UK

    C. Fernandez

Authors
  1. L. Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W. Shunli
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Fernandez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to W. Shunli.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, L., Shunli, W., Jiang, H. et al. An adaptive fractional-order unscented Kalman filter for Li-ion batteries in the energy storage system. Indian J Phys 96, 3933–3939 (2022). https://doi.org/10.1007/s12648-022-02314-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12648-022-02314-2

Keywords

Search

Navigation