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http://dx.doi.org/10.1007/s43236-022-00428-8

State-of-health estimation for lithium-ion batteries using differential thermal voltammetry and Gaussian process regression  

Wang, Ping (Department of Electrical Engineering, Tianjin University)
Peng, Xiangyuan (Department of Electrical Engineering, Tianjin University)
Ze, Cheng (Department of Electrical Engineering, Tianjin University)
Publication Information
Journal of Power Electronics / v.22, no.7, 2022 , pp. 1165-1175 More about this Journal
Abstract
The state of health (SOH) of lithium-ion batteries is a key factor to ensure the safe and reliable operation of electric vehicles (EVs). Differential thermal voltammetry (DTV) has been shown to be an effective in situ diagnosis method. In this paper, a SOH estimation method for lithium-ion batteries by a fusion of DTV and Gaussian process regression (GPR) is proposed. First, a wavelet transform (WT) is used to smooth DTV curves. Then the health features (HFs) are extracted, and their correlation with capacity loss is quantitatively evaluated through a Pearson correlation coefficient. To compress the data, canonical correlation analysis (CCA) is applied to obtain the canonical health feature (CHF). Subsequently, the GPR algorithm is used to establish the nonlinear mapping relationship between CHF and SOH. Experiments are performed on two datasets at different temperatures. Results show that the proposed method has high estimation accuracy and robustness.
Keywords
Lithium-ion battery; State of health; Differential thermal voltammetry; Canonical correlation analysis; Gaussian process regression;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Maher, K., Yazami, R.: A study of lithium ion batteries cycle aging by thermodynamics techniques. J. Power Sources 247, 527-533 (2014)   DOI
2 Merla, Y., Wu, B., Yuft, V.: Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries. J. Power Sources 307, 308-319 (2016)   DOI
3 Saha, B., Goebel, K.: Battery data set. NASA Ames Prognostics Data Repository, 2007. [Online]. Available: http://ti.arc.nasa.gov/project/prognostic-data-repository.
4 Bian, X, L., Wei, Z, B., He, J, T.: A two-step parameter optimization method for low-order model-based state of charge estimation. IEEE Transactions on Transportation Electrification 7(2), 399-409(2020).   DOI
5 Hu, X., Zhou, C., Li, Y.: Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs. IEEE Power Energ. Mag. 15(5), 20-31 (2017)   DOI
6 Farmann, A., Wang, W., Alexander, A.: Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles. J. Power Sources 281, 114-130 (2015)   DOI
7 Zou, C., Manzie, C., Nesic, D.: Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery. J. Power Sources 335, 121-130 (2016)   DOI
8 Hariharan, K.-S.: A nonlinear equivalent circuit model for lithium ion cells. J. Power Sources 222, 210-217 (2013)   DOI
9 Xing, Y.-J., Ma, E.-W.-M., Tsui, K.-L.: An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectron. Reliab. 53(6), 811-820 (2013)   DOI
10 Sepasi, S., Ghorbani, R., Liaw, B.-Y.: Inline state of health estimation of lithium-ion batteries using state of charge calculation. J. Power Sources 299, 246-254 (2015)   DOI
11 Wu, J., Zhang, C., Chen, Z.: An online method for lithium-ion battery remaining useful life estimation using importance sampling and neural networks. Appl. Energy 173, 134-140 (2016)   DOI
12 Birkl, C.: Oxford battery degradation dataset 1. Univ. Oxford, Oxford, U.K. (2017)
13 Zhou, X., Pan, Z., Han, X.: An easy-to-implement multi-point impedance technique for monitoring aging of lithium-ion batteries. J. Power Sources 417, 188-192 (2019)   DOI
14 Dubarry, M., Truchot, C., Liaw, B.-Y.: Synthesize battery degradation modes via a diagnostic and prognostic model. J. Power Sources 219(12), 204-216 (2012)   DOI
15 Liu, J., Chen, Z.: Remaining useful life prediction of lithium-ion batteries based on health indicator and Gaussian process regression model. IEEE Access 7, 39474-39484 (2019)   DOI
16 Bian, X. L., Wei, Z. B., He, J. T.: A novel model-based voltage construction method for robust state-of-health estimation of lithium-ion batteries. IEEE Trans. Ind. Electron. 68(12), 12173-12184 (2020)   DOI
17 Weng, C.-B., Feng, X.-N., Sun, J.: State-of-health monitoring of lithium-ion battery modules and packs via incremental capacity peak tracking. Appl. Energy 180, 360-368 (2016)   DOI
18 He, J.T., Wei, Z.B., Bian, X.L.: State-of-health estimation of lithium-ion batteries using incremental capacity analysis based on voltage-capacity model. IEEE Trans. Transp. Electrificat. 6(2), 417-426 (2020)   DOI
19 Stiaszny, B., Ziegler, J.C., Krauss, E.E.: Electrochemical characterization and post-mortem analysis of aged LiMn2O4-NMC/graphite lithium ion batteries part II: Calendar aging. J. Power Sources 258, 61-75 (2014)   DOI
20 Wu, B., Yuft, V., Merla, Y.: Differential thermal voltammetry for tracking of degradation in lithium-ion batteries. J. Power Sources 273, 495-501 (2015)   DOI
21 Wei, Z. B., Ruan, H. K., Li, Y.: Multi-stage state of health estimation of lithium-ion battery with high tolerance to heavily-partial charging (2022)
22 Nuhic, A., Terzimehic, T., Soczka-Guth, T.: Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. J. Power Sources 239, 680-688 (2013)   DOI
23 Li, Y., Liu, K.-L.: Data-driven health estimation and lifetime prediction of lithium-ion batteries: a review. Renew. Sustain. Energy Rev. 113, 68-85 (2019)
24 Ruan, H. K., He, H. W., Wei, Z. B.: State of health estimation of lithium-ion battery based on constant-voltage charging reconstruction. IEEE J. Emerg. Select. Top. Power Electron. (2021)
25 Zhou, Y., Huang, M., Chen, Y.: A novel health indicator for online lithium-ion batteries remaining useful life prediction. J. Power Sources 321, 1-10 (2016)   DOI
26 Smith, A. J., Dahn, H. M., Burns, J. C.: Long-term low-rate cycling of LiCoO2graphite li-ion cells at 55℃. J. Electrochem. Soc. 159(6), A705-A710 (2012)   DOI
27 Bian, X. L., Wei, Z. B., Li, W. H.: State-of-health estimation of lithium-ion batteries by fusing an open circuit voltage model and incremental capacity analysis. IEEE Trans. Power Electron. 37(2), 2226-2236 (2022)