참고문헌
- 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). https://doi.org/10.1109/TTE.2020.3032737
- 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) https://doi.org/10.1109/MPE.2017.2708812
- 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) https://doi.org/10.1016/j.jpowsour.2015.01.129
- 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) https://doi.org/10.1016/j.jpowsour.2016.10.040
- Hariharan, K.-S.: A nonlinear equivalent circuit model for lithium ion cells. J. Power Sources 222, 210-217 (2013) https://doi.org/10.1016/j.jpowsour.2012.08.090
- 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) https://doi.org/10.1016/j.jpowsour.2015.08.091
- 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) https://doi.org/10.1016/j.microrel.2012.12.003
- 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) https://doi.org/10.1016/j.apenergy.2016.04.057
- 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) https://doi.org/10.1016/j.jpowsour.2012.11.146
- 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)
- 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)
- 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)
- 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) https://doi.org/10.1109/access.2019.2905740
- 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) https://doi.org/10.1016/j.jpowsour.2016.04.119
- 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) https://doi.org/10.1016/j.jpowsour.2014.02.019
- 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) https://doi.org/10.1109/TIE.2020.3044779
- 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) https://doi.org/10.1149/2.056206jes
- 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) https://doi.org/10.1016/j.jpowsour.2018.11.087
- 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) https://doi.org/10.1016/j.jpowsour.2012.07.016
- 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) https://doi.org/10.1016/j.apenergy.2016.07.126
- 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) https://doi.org/10.1109/TTE.2020.2994543
- 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)
- Wu, B., Yuft, V., Merla, Y.: Differential thermal voltammetry for tracking of degradation in lithium-ion batteries. J. Power Sources 273, 495-501 (2015) https://doi.org/10.1016/j.jpowsour.2014.09.127
- Maher, K., Yazami, R.: A study of lithium ion batteries cycle aging by thermodynamics techniques. J. Power Sources 247, 527-533 (2014) https://doi.org/10.1016/j.jpowsour.2013.08.053
- 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) https://doi.org/10.1016/j.jpowsour.2015.12.122
- Birkl, C.: Oxford battery degradation dataset 1. Univ. Oxford, Oxford, U.K. (2017)
- Saha, B., Goebel, K.: Battery data set. NASA Ames Prognostics Data Repository, 2007. [Online]. Available: http://ti.arc.nasa.gov/project/prognostic-data-repository.