Browse > Article
http://dx.doi.org/10.1007/s43236-021-00318-5

SOH and RUL prediction of Li-ion batteries based on improved Gaussian process regression  

Feng, Hailin (School of Mathematics and Statistics, Xidian University)
Shi, Guoling (School of Mathematics and Statistics, Xidian University)
Publication Information
Journal of Power Electronics / v.21, no.12, 2021 , pp. 1845-1854 More about this Journal
Abstract
Accurately predicting the state of health (SOH) and remaining useful life (RUL) of Li-ion batteries is the key to Li-ion battery health management. In this paper, a novel GPR-based method for SOH and RUL prediction is proposed. First, five features are extracted from the cyclic charging currents of batteries, and a grey correlation analysis (GRA) shows that these five features are highly correlated with battery capacity. A novel Li-ion battery SOH prediction model is established by improving a basic Gaussian process regression model. Meanwhile, a polynomial regression model is developed to update the feature values in the future. Then the RUL of a battery is predicted by combining the SOH prediction model. Finally, the prediction effect of the proposed model is compared with other models using four Li-ion battery degradation data. The obtained results show that the model proposed in this paper has the highest accuracy. The robustness of the proposed model is verified by random walk battery data.
Keywords
Li-ion batteries; State-of-health; Remaining useful life; Gaussian process regression; Polynomial regression; Charging current;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Bian, X., Liu, L., Yan, J., et al.: An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries: model development and validation. J. Power Sources 448, 227401 (2020). https://doi.org/10.1016/j.energy.2019.04.070   DOI
2 Li, S., Wang, B., Peng, H., et al.: An electrochemistry-based impedance model for lithium-ion batteries. J. Power Sources 258, 9-18 (2014)   DOI
3 Ruan, H., He, H., Wei, Z., et al.: State of health estimation of lithium-ion battery based on constant-voltage charging reconstruction. IEEE J. Emerg. Sel. Top. Power Electron. (2021). https://doi.org/10.1109/JESTPE.2021.3098836   DOI
4 Wei, J., Dong, G., Chen, Z.: Remaining useful life prediction and state of health diagnosis for Lithium-Ion batteries using particle filter and support vector regression. IEEE Trans. Industr. Electron. 65(7), 5634-5643 (2018)   DOI
5 Nuhic, A., Terzimehic, T., Soczka-Guth, T., et al.: Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. J. Power Sources 239, 680-688 (2013)   DOI
6 He, J., Wei, Z., Bian, X., et al.: State-of-health estimation of Lithium-Ion batteries using incremental capacity analysis based on voltage-capacity model. IEEE Trans. Transp. Electrif. 6(2), 417-426 (2020). https://doi.org/10.1109/TTE.2020.2994543   DOI
7 Akash, B., Zineb, S., Eric, G., et al.: Review on State of Health estimation methodologies for lithium-ion batteries in the context of circular economy. CIRP J. Manuf. Sci. Technol. 32, 517-528 (2021)   DOI
8 Yu, Z., Xiao, L., Li, H., et al.: Model parameter identification for lithium batteries using the coevolutionary particle swarm optimization method. IEEE Trans. Ind. Electron. 64(7), 5690-5700 (2017)   DOI
9 Bian, X., Liu, L., Yan, J.: A model for state-of-health estimation of lithium ion batteries based on charging profiles. Energy 177, 57-65 (2019). https://doi.org/10.1016/j.jpowsour.2019.227401   DOI
10 Zhou, Y., Huang, M.: Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model. Microelectron. Reliab. 65, 265-273 (2016)   DOI
11 Dong, G., Yang, F., Wei, Z., et al.: Data-driven battery health prognosis using adaptive brownian motion model. IEEE Trans. Industr. Inf. 16(7), 4736-4746 (2020)   DOI
12 Zhou, Y., Huang, M., Chen, Y., et al.: A novel health indicator for on-line lithium-ion batteries remaining useful life prediction. J. Power Sources 321(6), 1-10 (2016)   DOI
13 Liu, D., Zhou, J., Liao, H., et al.: A Health Indicator Extraction and Optimization Framework for Lithium-Ion Battery Degradation Modeling and Prognostics. IEEE Trans. Syst. Man Cybern.: Syst. 45(6), 915-928 (2015)   DOI
14 Wang, R., Feng, H.: Remaining useful life prediction of lithium-ion battery using a novel health indicator. Qual. Reliab. Eng. Int. 37(3), 1232-1243 (2021)   DOI
15 Li, X., Yuan, C., Wang, Z., et al.: Multi-time-scale framework for prognostic health condition of lithium battery using modified Gaussian process regression and nonlinear regression. J. Power Sources 467, 228358 (2020)   DOI
16 Piao, C., Li, Z., Lu, S., et al.: Analysis of real-time estimation method based on hidden markov models for battery system states of health. J. Power Electron. 16(1), 217-226 (2016)   DOI
17 Feng, H., Song, D.: A health indicator extraction based on surface temperature for lithium-ion batteries remaining useful life prediction. J. Energy Storage 34, 102118 (2021)   DOI
18 Wang, R., Feng, H.: Lithium-ion batteries remaining useful life prediction using Wiener process and unscented particle filter. J. Power Electron. 20, 270-278 (2020)   DOI
19 Dong, G., Chen, Z., Wei, J., et al.: Battery health prognosis using brownian motion modeling and particle filtering. IEEE Trans. Industr. Electron. 65(11), 8646-8655 (2018)   DOI
20 Li, X., Wang, Z., Yan, J., et al.: Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression. J. Power Sources 421, 56-67 (2019)   DOI
21 Liu, D., Pang, J., Zhou, J., et al.: Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression. Microelectron. Reliab. 53(6), 832-839 (2013)   DOI
22 He, Z., Gao, M., Ma, G., et al.: Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks. J. Power Sources 267, 576-583 (2014)   DOI
23 Liang, J., Shi, J., Wen, Y., et al.: SOH and RUL prediction of lithium-ion batteries based on Gaussian process regression with indirect health indicators. Energies 13(2), 375 (2020)   DOI
24 Yang, D., Zhang, X., Pan, R., et al.: A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve. J. Power Sources 384, 387-395 (2018)   DOI
25 Goebelk, S., Saxena, A., et al.: Prognostics in battery health management. IEEE Instrum. Meas. Mag. 11(4), 33-40 (2008)   DOI
26 Bole, B., Kulkarni, C.S., Daigle, M.: Adaptation of an electrochemistry-based Li-ion battery model to account for deterioration observed under randomized use. SGT. Inc. Moffett. Field, United States (2014). https://doi.org/10.36001/phmconf.2014.v6i1.2490   DOI
27 Schwunk, S., Armbruster, N., Straub, S., et al.: Particle filter for state of charge and state of health estimation for lithium-iron phosphate batteries. J. Power Sources. 239, 705-710 (2013)   DOI
28 Li, X., Wang, Z., Zhang, L., et al.: State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis. J. Power Sources 410-411, 106-114 (2019)   DOI
29 Tosun, N.: Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. Int. J. Adv. Manuf. Technol. 28(5-6), 450-455 (2006)   DOI
30 Bai, G., Wang, P., Hu, C., et al.: A generic model-free approach for lithium-ion battery health management. Appl. Energy 135, 247-260 (2014)   DOI
31 Bian, X., Wei, Z., He, J., et al.: A novel model-based voltage construction method for robust state-of-health estimation of lithium-ion batteries. IEEE Trans. Ind. Electron. (2020). https://doi.org/10.1109/TIE.2020.3044779   DOI