Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Fast cycle life evaluation method for ternary lithium-ion batteries based on divided SOC intervals

  • Wang, Qiuting (School of Information and Electronic Engineering, Zhejiang University City College) ;
  • Sun, Jiani (Zhejiang Construction Engineering Group Co., Ltd) ;
  • Wu, Hong (Keyi College of Zhejiang Sci-Tech University) ;
  • Qi, Wei (School of Information and Electronic Engineering, Zhejiang University City College) ;
  • Jin, Hui (School of Information and Electronic Engineering, Zhejiang University City College) ;
  • Ling, Li (School of Information and Electronic Engineering, Zhejiang University City College)
  • Received : 2021.11.01
  • Accepted : 2022.02.07
  • Published : 2022.05.20
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Abstract

Ternary lithium-ion batteries are commonly used in electrical power systems. It is necessary to accurately estimate the life characteristics of the battery cell/pack under specific cycle conditions. In this article, the empirical model of the capacity attenuation value is improved, and a mathematical model of the capacity attenuation rate is established. The cell capacity value based on the entire state of charge (SOC) interval and the divided SOC intervals are identified. The difference between them is calculated and analyzed. A multiple regression model is presented to describe the linear relationship between the health state parameters and the capacity value of a ternary lithium-on battery. Finally, an accelerated aging test is introduced, and a fast cycle life evaluation method is proposed. The proposed method is validated with experimental data under different charge/discharge currents. The obtained results show that the new model and the algorithms trained by divided SOC intervals are effective. They also show that the cycle life estimation error can be ranged within 3%. In addition, the cycle time of the aging test can be reduced to about one fifth of that with the traditional method.

Keywords

References

  1. Tang X H, Ma X, et al: Research progress of battery management system for electric vehicles. 42(2), 308-311 (2018)
  2. Zheng, Q., Watanabe, M., et al.: Hydrothermal leaching of ternary and binary lithium-ion battery cathode materials with citric acid and the kinetic study. J Supercritical Fluids 165, 1-9 (2020)
  3. Yang, X., Leng, Y., Zhang, G., et al.: Modeling of lithium plating induced aging of lithium-ion batteries: transition from linear to nonlinear aging. J Power Sources 360, 28-40 (2017) https://doi.org/10.1016/j.jpowsour.2017.05.110
  4. Han, B., Liao, C., Dogan, F., et al.: Using mixed salt electrolytes to stabilize silicon anodes for lithium-ion batteries via In situ formation of Li-M-Si ternaries (M=Mg, Zn, Al, Ca). ACS Appl Mater Interfaces 11, 29780-29790 (2019) https://doi.org/10.1021/acsami.9b07270
  5. Su, L., Zhang, J., Wang, C., et al.: Identifying main factors of capacity fading in Lithium ion cell using orthogonal design of experiments. Appl Energy 163, 201-210 (2016) https://doi.org/10.1016/j.apenergy.2015.11.014
  6. Kikuchi, Y., Suwa, I., Heiho, A., et al.: Separation of cathode particles and aluminum current foil in lithium-ion battery by high-voltage pulsed discharge Part II: Prospective life cycle assessment based on experimental data. Waste Manage 132, 86-95 (2021) https://doi.org/10.1016/j.wasman.2021.07.016
  7. He, J.T., Wei, Z.B., Bian, X.L., et al.: State-of-Health estimation of lithium-ion batteries using incremental capacity analysis based on voltage-capacity model. IEEE Trans Transp Electr 6, 417-426 (2020) https://doi.org/10.1109/tte.2020.2994543
  8. Somerville, L., Barenp, J., Trask, S., et al.: The effect of charging rates on the graphite electrode of commercial lithium-ion cells: a post-mortem study. J Power Sources 335, 189-196 (2016) https://doi.org/10.1016/j.jpowsour.2016.10.002
  9. Wu, Z., Cao, C., Yan, X., et al.: Effects of charge cut-of voltage on the performances of monocrystalline LiNiO5Coo2MnO3O2/graphite Li-ion cells. Electrochim Acta 302, 153-160 (2019) https://doi.org/10.1016/j.electacta.2019.01.181
  10. Saxena, S., Hendricks, C., Pecht, M., et al.: Cycle life testing and modeling of graphite/LiCoCb cells under different state of charge ranges. J Power Sources 327, 394-400 (2016) https://doi.org/10.1016/j.jpowsour.2016.07.057
  11. Gao, Y., Yang, S., Jiang, J., et al.: The mechanism and characterization of accelerated capacity deterioration for lithium-ion battery with Li(NiMnCo) O2 cathode. J Electrochem Soc 166, A1623-A1635 (2019) https://doi.org/10.1149/2.1001908jes
  12. Profatilova, I., Vito, E.D., Genies, S., et al.: Impact of silicon/ graphite composite electrode porosity on the cycle life of 18650 lithium-ion cell. ACS Appl Energy Mater. 3(12), 11873-11885 (2020) https://doi.org/10.1021/acsaem.0c01999
  13. Wei, Z.B., Hu, J., He, H.W., et al.: Load current and state-of-charge coestimation for current sensor-free lithium-ion battery. IEEE Trans Power Electron. 36, 10970-10975 (2021) https://doi.org/10.1109/TPEL.2021.3068725
  14. Bian, X.L., Wei, Z.B., He, J.T., et al.: A two-step parameter optimization method for low-order model-based state-of-charge estimation. IEEE Trans Transp Electrifc 7, 399-409 (2021) https://doi.org/10.1109/TTE.2020.3032737
  15. Huanhuan, L., Chengyang, L., et al.: Coupling multi-physics simulation and response surface methodology for the thermal optimization of ternary prismatic lithium-ion battery. J Power Sources 438, 1-13 (2019)
  16. Yang, X., Wang, C.: Understanding the trilemma of fast charging, energy density and cycle life of lithium-ion batteries. J Power Sources 402, 489-498 (2018) https://doi.org/10.1016/j.jpowsour.2018.09.069
  17. Ruan, Y., Song, X., Fu, Y., et al.: Structural evolution and capacity degradation mechanism of LiNiO6MnO2Coo2O2 cathode materials. J Power Sources 400, 539-548 (2018) https://doi.org/10.1016/j.jpowsour.2018.08.056
  18. Tian J Q, Xu R L, Wang Y J, et al: Capacity attenuation mechanism modeling and health assessment of lithium-ion batteries. Energy. 221, article 119682 (2021)
  19. Fan L F, Wang P, Cheng Z: A remaining capacity estimation approach of lithium-ion batteries based on partial charging curve and health feature fusion. Journal of Energy Storage. 43, article 103115 (2021)
  20. Prang, A., Kersys, A., Kriston, A.: Long-term cycling induced jelly roll deformation in commercial 18650 cells. J Power Sources 392, 168-175 (2018) https://doi.org/10.1016/j.jpowsour.2018.03.065
  21. Zhou, X., Pan, Z., Han, X., et al.: An easy-to-implement multipoint 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
  22. Rui, X., Li, L., Li, Z., et al.: An electrochemical model based degradation state identification method of lithium-ion battery for all-climate electric vehicles application. Appl Energy 219, 264-275 (2018) https://doi.org/10.1016/j.apenergy.2018.03.053
  23. Kato, H., Kobayashi, Y., Miyashiro, H.: Differential voltage curve analysis of a lithium-ion battery during discharge. J Power Sources 398, 49-54 (2018) https://doi.org/10.1016/j.jpowsour.2018.07.043
  24. Louli, A.J., Ellis, L.D., Dahn, J.R.: Operando pressure measurements reveal solid electrolyte interphase growth to rank li-ion cell performance. Joule 3, 1-17 (2019) https://doi.org/10.1016/j.joule.2018.12.022
  25. Li, X., Wang, Q., Yang, Y., et al.: Correlation between capacity loss and measurable parameters of lithium-ion batteries. Electr Power Energy Syst 110, 819-826 (2019) https://doi.org/10.1016/j.ijepes.2019.03.046
  26. Yang B, Wang J B, Zhang X S, et al: Comprehensive overview of meta-heuristic algorithm applications on PV cell parameter identification. Energy Conversion and Management, 205, article 112595 (2020)
  27. Cadini, F., Sbarufatti, C., Cancelliere, F., et al.: State-of-life prognosis and diagnosis of lithium-ion batteries by data-driven particle filters. Appl Energy 235, 661-672 (2019) https://doi.org/10.1016/j.apenergy.2018.10.095
  28. Ye, M., Guo, H., Xiong, R., et al.: A double-scale and adaptive particle filter-based online parameter and state of charge estimation method for lithium-ion batteries. Energy 114, 789-799 (2018)
  29. Li, J., Lin, C., Chen, K.: Cycle life prediction of aged lithium-ion batteries from the fading trajectory of a four-parameter model. J Electrochem Soc 165(16), 3634-3641 (2018)
  30. Pang, H., Mou, L., Guo, L., et al.: Parameter identification and systematic validation of an enhanced Single-particle model with aging degradation physics for Li-ion batteries. Electrochim Acta 307, 474-487 (2019) https://doi.org/10.1016/j.electacta.2019.03.199