Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
권호 표지
DOI QR코드

DOI QR Code

Remaining useful life prediction for lithium-ion battery using dynamic fractional brownian motion degradation model with long-term dependence

  • Xin, Li (Department of Control Science and Engineering, Jilin University) ;
  • Yan, Ma (Department of Control Science and Engineering, Jilin University)
  • Received : 2022.05.17
  • Accepted : 2022.07.28
  • Published : 2022.12.20
⟨ Previous Next ⟩

Abstract

The remaining useful life (RUL) prognostic of lithium-ion batteries (LIBs) is important in the reliability of electric vehicles. The degradation state of LIBs is related to the current moment and historical data, which is a non-Markovian process with long-term dependence. This manuscript proposes a RUL prognostic approach based on a non-Markovian process, which uses a fractional Brownian motion (FBM) model. Firstly, a nonlinear FBM model is established to describe the battery non-Markovian capacity fading process. The drift parameter of the FBM model and degradation states are updated by an online Kalman filter when a new measurement value arrives. Then the maximum likelihood estimation approach is introduced to obtain the other undecided fixed parameters. This approach is based on of-line battery degradation historical data. According to the first hitting time, the probability distribution function is derived to quantify the uncertainty of the RUL prognostic results. Finally, two datasets are used to verify the effectiveness of the proposed method. For the NASA dataset battery #5, the relative errors of the RUL prediction results of the proposed method are 2.941 and 2.083 when the starting points of the predictions are 60 cycles and 80 cycles, respectively. Thus, the proposed method is superior to other methods.

Keywords

Acknowledgement

This work was supported by the Scientific and Technological Research Program of the Jilin Province Department of Education [Grant 20190016KJ], and the National Nature Science Foundation of China [Grant 61520106008].

References

  1. Xiong, R., Pan, Y., Shen, W., et al.: Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives. Renew Sustain Energy Rev. 131, 110048 (2020)
  2. Lipu, M.S.H., Hannan, M.A., Hussain, A., et al.: A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations. J. Cleaner Prod. 205, 115-133 (2018) https://doi.org/10.1016/j.jclepro.2018.09.065
  3. Ungurean, L., Carstoiu, G., Micea, M.V., et al.: Battery state of health estimation: a structured review of models, methods and commercial devices. Int. J. Energy Res. 41(2), 151-181 (2017) https://doi.org/10.1002/er.3598
  4. Feng, H., Shi, G.: SOH and RUL prediction of Li-ion batteries based on improved Gaussian process regression. J. Power Electron. 21, 1845-1854 (2021) https://doi.org/10.1007/s43236-021-00318-5
  5. Ma, Y., Chen, Y., Zhou, X., et al.: Remaining useful life prediction of lithium-ion battery based on Gauss-Hermite particle filter. IEEE Trans. Control Syst. Technol. 27(4), 1788-1795 (2019) https://doi.org/10.1109/tcst.2018.2819965
  6. Severson, K.A., Attia, P.M., Jin, N., et al.: Data-driven prediction of battery cycle life before capacity degradation. Nat. Energy 4(5), 383-391 (2019) https://doi.org/10.1038/s41560-019-0356-8
  7. Zheng, X., Fang, H.: An integrated unscented Kalman filter and relevance vector regression approach for lithium-ion battery remaining useful life and short-term capacity prediction. IEEE Trans. Ind. Electron. 144, 74-82 (2015)
  8. Sihvo, J., Roinila, T., Stroe, D.-I.: Novel fitting algorithm for parametrization of equivalent circuit model of li-ion battery from broadband impedance measurements. IEEE Trans. Ind. Electron. 68(6), 4916-4926 (2021) https://doi.org/10.1109/TIE.2020.2988235
  9. Ma, Y., Li, X., Li, G., et al.: SOC oriented electrochemical-thermal coupled modeling for lithium-ion battery. IEEE Access 7, 156136 (2019)
  10. Hussein, A.A.: Capacity fade estimation in electric vehicle li-ion batteries using artificial neural networks. IEEE Trans. Ind. Appl. 51(3), 677-681 (2015) https://doi.org/10.1109/TIA.2014.2365152
  11. Liu, H., Song, W., Niu, Y., et al.: A generalized Cauchy method for remaining useful life prediction of wind turbine gearboxes. Mech. Syst. Signal Process 153, 107471 (2021)
  12. Motahari-Nezhad, M., Jafari, S.M.: Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing. Expert Syst. Appl. 168, 114391 (2021)
  13. Zhai, Q., Ye, Z.: RUL prediction of deteriorating products using an adaptive Wiener process model. IEEE Trans. Industr. Inform. 13(6), 2911-2921 (2017) https://doi.org/10.1109/TII.2017.2684821
  14. Han, Y., Ma, C., Tang, S., Wang, F., Sun, X., Si, X.: Residual life estimation of lithium-ion batteries based on nonlinear Wiener process with measurement error. P. I. Mech. Eng. O-J. Ris. 1748006X221080345 (2022)
  15. Si, X.: An adaptive prognostic approach via nonlinear degradation modeling: Application to battery data. IEEE Trans. Ind. Electron. 62(8), 5082-5096 (2015) https://doi.org/10.1109/TIE.2015.2393840
  16. Dong, G., Chen, Z., Wei, J., et al.: Battery health prognosis using Brownian motion modeling and particle filtering. Trans. Ind. Electron. 65(11), 8646-8655 (2018) https://doi.org/10.1109/TIE.2018.2813964
  17. Li, T., Pei, H., Pang, Z., et al.: A sequential Bayesian updated Wiener process model for remaining useful life prediction. IEEE Access 8, 5471-5480 (2020) https://doi.org/10.1109/access.2019.2962502
  18. Kong, J., Wang, D., Yan, T., Zhu, J., Zhang, X.: Accelerated stress factors based nonlinear wiener process model for lithium-ion battery prognostics. IEEE Trans. Ind. Electron. 69(11), 11665-11674 (2022) https://doi.org/10.1109/TIE.2021.3127035
  19. Zhang, S., Zhai, Q., Shi, X., Liu, X.: A Wiener Process Model with Dynamic Covariate for Degradation Modeling and Remaining Useful Life Prediction. IEEE Trans. Reliab. (2022) https://doi.org/10.1109/tr.2003.821922
  20. Zhang, H., Chen, M., Shang, J., Yang, C., Sun, Y.: Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion. Sci. China Inf. Sci. 64(7), 171201 (2021)
  21. Zhang, H., Jia, C., Chen, M.Y.: Remaining useful life prediction for degradation processes with dependent and nonstationary increments. IEEE Trans. Instrum. Meas. 70, 3519212 (2021)
  22. Xi, X., Chen, M., Zhang, H., et al.: An improved non-Markovian degradation model with long-term dependency and item-to-item uncertainty. Mech. Syst. Signal Process 105, 467-480 (2018) https://doi.org/10.1016/j.ymssp.2017.12.017
  23. Zhang, H., Zhou, D., Chena, M., et al.: Predicting remaining useful life based on a generalized degradation with fractional Brownian motion. Mech. Syst. Signal Process 115, 736-752 (2019) https://doi.org/10.1016/j.ymssp.2018.06.029
  24. Reis, G., Strange, C., Yadav, M., Li S.: Lithium-ion battery data and where to find it. Energy AI 5, 100081 (2021)
  25. Wang, H., Song, W., Zio, E., et al.: Remaining useful life prediction for lithium-ion batteries using fractional Brownian motion and fruit-fly optimization algorithm. Measurement 161, 107904 (2020)
  26. Wang, D., Tsui, K.L.: Brownian motion with adaptive drift for remaining useful life prediction: Revisited. Mech. Syst. Signal Process 99, 691-701 (2018) https://doi.org/10.1016/j.ymssp.2017.07.015
  27. Zhang, H., Zhou, D., Chen, M., et al.: FBM-based remaining useful life prediction for degradation processes with long-range dependence and multiple modes. IEEE Trans. Reliab. 68(3), 1021-1033 (2019) https://doi.org/10.1109/tr.2018.2877643
  28. Xi, X., Zhou, D., Chen, M., et al.: Remaining useful life prediction for fractional degradation processes under varying modes. Can. J. Chem. Eng. 98(6), 1351-1364 (2020) https://doi.org/10.1002/cjce.23666
  29. Sottinen, T.: Fractional Brownian motion, random walks and binary market models. Finance Stoch. 5(3), 343-355 (2001) https://doi.org/10.1007/PL00013536
  30. Konstantopoulos, T., Sakhanenko, A.: Convergence and convergence rate to fractional Brownian motion for weighted random sums. A. Sakhanenko. Sib. Electron. Math. Re. 1, 47-63 (2004)
  31. Zhang, H., Chen, M., Xi, X., et al.: Remaining useful life prediction for degradation processes with long-range dependence. IEEE Trans. Reliab. 66(4), 1368-1379 (2017) https://doi.org/10.1109/TR.2017.2720752
  32. Li, X., Ma, Y., Zhu, J.: An online dual filters RUL prediction method of lithium-ion battery based on unscented particle filter and least squares support vector machine. Measurement 184, 109935 (2021)
  33. Liu, D., Luo, Y., Liu, J., Peng, Y., Guo, L., Pecht, M.: Lithium-ion battery remaining useful life estimation based on fusion nonlinear degradation AR model and RPF algorithm. Neural Comput. Appl. 25, 557-572 (2014) https://doi.org/10.1007/s00521-013-1520-x
  34. Song, Y., Liu, D., Hou, Y., Yu, J., Peng, Y.: Satellite lithium-ion battery remaining useful life estimation with an iterative updated RVM fused with the KF algorithm. Chin. J. Aeronaut. 31(1), 31-40 (2018) https://doi.org/10.1016/j.cja.2017.11.010