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Online cell-by-cell SOC/SOH estimation method for battery module employing extended Kalman filter algorithm with aging effect consideration

  • Ngoc-Thao, Pham (Department of Electrical, Electronic and Computer Engineering, University of Ulsan) ;
  • Phuong-Ha, La (Department of Electrical, Electronic and Computer Engineering, University of Ulsan) ;
  • Sung-Jin, Choi (Department of Electrical, Electronic and Computer Engineering, University of Ulsan)
  • Received : 2022.07.25
  • Accepted : 2022.09.13
  • Published : 2022.12.20

Abstract

As the number of series connections of battery cells increases, individual cells are operating in different temperature profiles, and the aging patterns of the cells become dissimilar from each other. Thenceforth, individual state-cell-characteristics should be tracked online for higher safety. Although Kalman-filter-based battery state estimation is one of the most popular methods, it is sensitive to the accuracy of the battery model parameters and difficult to be applied to every cell. This work proposes an online cell-by-cell state-of-charge (SOC)/state-of-health (SOH) estimation method to mitigate this limitation. The aging patterns of the individual cells are predicted by introducing a combination of a switch-matrix flying capacitor and electrochemical impedance spectroscopy (EIS) model parameter scanning techniques. Accordingly, the accuracy of the SOC estimation for individual cells is enhanced. The proposed method is verified by a real-time simulation platform, where the SOC and SOH levels of the cells are individually estimated within a 1.24% error.

Keywords

Acknowledgement

This work was partly supported by the Technology Development Program (S3207312) funded by the Ministry of SMEs and Startups (MSS, Korea) and the National Research Foundation of Korea (NRF-2020R1A2C2009303) grant funded by the Korea government (MSIT).

References

  1. Castillo, G.A.L., Alava, L.A.C., Arauz, W.M.S., Rodriguez, J.A.P.: Proposal of photovoltaic system for house. Int. J. Phys. Sci. Eng. 4(2), 26-35 (2020)
  2. Kim, J., Cho, B.H.: Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation. Energy 57, 581-599 (2013) https://doi.org/10.1016/j.energy.2013.04.050
  3. Dong, H., Huang, W., Zhao, Y.: Low complexity state-of-charge estimation for lithium-ion battery pack considering cell inconsistency. J. Power Sources 515, 230599 (2021)
  4. Guo, R., Lu, L., Ouyang, M., Feng, X.: Mechanism of the entire overdischarge process and overdischarge-induced internal short circuit in lithium-ion batteries. Sci. Rep. 6(1), 1-9 (2016) https://doi.org/10.1038/s41598-016-0001-8
  5. Zhang, C., Jiang, Y., Jiang, J., Cheng, G., Diao, W., Zhang, W.: Study on battery pack consistency evolutions and equilibrium diagnosis for serial-connected lithium-ion batteries. Appl. Energy 207, 510-519 (2017) https://doi.org/10.1016/j.apenergy.2017.05.176
  6. Martinez-Laserna, E., Gandiaga, I., Sarasketa-Zabala, E., Badeda, J., Stroe, D.I., Swierczynski, M., Goikoetxea, A.: Battery second life: hype, hope or reality? A critical review of the state of the art. Renew. Sustain. Energy Rev. 93, 701-718 (2018) https://doi.org/10.1016/j.rser.2018.04.035
  7. Chen, Y., Kang, Y., Zhao, Y., Wang, L., Liu, J., Li, Y., et al.: A review of lithium-ion battery safety concerns: the issues, strategies, and testing standards. J. Energy Chem. 59, 83-99 (2021) https://doi.org/10.1016/j.jechem.2020.10.017
  8. Park, S., Ahn, J., Kang, T., Park, S., Kim, Y., Cho, I., Kim, J.: Review of state-of-the-art battery state estimation technologies for battery management systems of stationary energy storage systems. J. Power Electron. 20(6), 1526-1540 (2020) https://doi.org/10.1007/s43236-020-00122-7
  9. Xiong, R., Cao, J., Yu, Q., He, H., Sun, F.: Critical review on the battery state of charge estimation methods for electric vehicles. IEEE Access 6, 1832-1843 (2017) https://doi.org/10.1109/access.2017.2780258
  10. Ng, K.S., Moo, C.S., Chen, Y.P., Hsieh, Y.C.: Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl. Energy 86(9), 1506-1511 (2009) https://doi.org/10.1016/j.apenergy.2008.11.021
  11. Zhao, L., Lin, M., Chen, Y.: Least-squares based coulomb counting method and its application for state-of-charge (SOC) estimation in electric vehicles. Int. J. Energy Res. 40(10), 1389-1399 (2016) https://doi.org/10.1002/er.3530
  12. Salkind, A.J., Fennie, C., Singh, P., Atwater, T., Reisner, D.E.: Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. J. Power Sources 80(1-2), 293-300 (1999) https://doi.org/10.1016/S0378-7753(99)00079-8
  13. Shen, W.X., Chan, C.C., Lo, E.W.C., Chau, K.T.: A new battery available capacity indicator for electric vehicles using neural network. Energy Convers. Manag. 43(6), 817-826 (2002) https://doi.org/10.1016/S0196-8904(01)00078-4
  14. Hansen, T., Wang, C.J.: Support vector based battery state of charge estimator. J. Power Sources 141(2), 351-358 (2005) https://doi.org/10.1016/j.jpowsour.2004.09.020
  15. Plett, G.L.: Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 3. State and parameter estimation. J. Power Sources 134(2), 277-292 (2004) https://doi.org/10.1016/j.jpowsour.2004.02.033
  16. Pulavarthi, C., Kalpana, R., & Parthiban, P.: State of Charge estimation in lithium-ion battery using model based method in conjunction with extended and unscented Kalman filter. In 2020 International Conference on Power Electronics and Renewable Energy Applications (PEREA), pp. 1-6. IEEE, November, 2020
  17. Jokic, I., Zecevic, Z., & Krstajic, B.: State-of-charge estimation of lithium-ion batteries using extended Kalman filter and unscented Kalman filter. In 2018 23rd International Scientific-Professional Conference on Information Technology (IT), pp. 1-4, February, 2018
  18. Khanum, F., Louback, E., Duperly, F., Jenkins, C., Kollmeyer, P. J., & Emadi, A.: A Kalman filter based battery state of charge estimation MATLAB function. In 2021 IEEE Transportation Electrification Conference & Expo (ITEC), pp. 484-489, IEEE, June, 2021
  19. Pop, V., Bergveld, H.J., Regtien, P.P., Het Veld, J.O., Danilov, D., Notten, P.H.L.: Battery aging and its influence on the electromotive force. J Electrochem Soc 154(8), A744 (2007)
  20. Xiong, R., Pan, Y., Shen, W., Li, H., Sun, F.: Lithium-ion battery aging mechanisms and diagnosis method for automotive applications: Recent advances and perspectives. Renew. Sustain. Energy Rev. 131, 110048 (2020)
  21. Kim, M., Kim, K., Kim, J., Yu, J., Han, S.: State of charge estimation for lithium ion battery based on reinforcement learning. IFAC PapersOnLine 51(28), 404-408 (2018) https://doi.org/10.1016/j.ifacol.2018.11.736
  22. You, G., Wang, X., Fang, C., Zhang, S., & Hou, X.: State of charge estimation of lithium-ion battery based on double deep Q network and extended Kalman filter. In IOP Conference Series: Earth and Environmental Science, Vol. 615, No. 1, pp. 012080. IOP Publishing, December, 2020
  23. Chui, C.K., Chen, G.: Extended Kalman filter and system identification. In: Kalman Filtering, pp. 115-137. Springer, Cham (2017)
  24. Gong, X., Xiong, R., Mi, C.C.: Study of the characteristics of battery packs in electric vehicles with parallel-connected lithium-ion battery cells. IEEE Trans. Ind. Appl. 51(2), 1872-1879 (2014) https://doi.org/10.1109/TIA.2014.2345951
  25. La, P.H., Choi, S.J.: Direct cell-to-cell equalizer for series battery string using switch-matrix single-capacitor equalizer and optimal pairing algorithm. IEEE Trans. Power Electron. 37(7), 8625-8639 (2022) https://doi.org/10.1109/TPEL.2022.3147842