Journal of Power Electronics

  • 제18권1호
  • /
  • Pages.129-136
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  • 2018
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  • 1598-2092(pISSN)
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  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
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State-of-charge Estimation for Lithium-ion Battery using a Combined Method

  • Li, Guidan (School of Electrical and Information Engineering, Tianjin University) ;
  • Peng, Kai (School of Electrical and Information Engineering, Tianjin University) ;
  • Li, Bin (School of Electrical and Information Engineering, Tianjin University)
  • 투고 : 2017.05.18
  • 심사 : 2017.08.27
  • 발행 : 2018.01.20
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초록

An accurate state-of-charge (SOC) estimation ensures the reliable and efficient operation of a lithium-ion battery management system. On the basis of a combined electrochemical model, this study adopts the forgetting factor least squares algorithm to identify battery parameters and eliminate the influence of test conditions. Then, it implements online SOC estimation with high accuracy and low run time by utilizing the low computational complexity of the unscented Kalman filter (UKF) and the rapid convergence of a particle filter (PF). The PF algorithm is adopted to decrease convergence time when the initial error is large; otherwise, the UKF algorithm is used to approximate the actual SOC with low computational complexity. The effect of the number of sampling particles in the PF is also evaluated. Finally, experimental results are used to verify the superiority of the combined method over other individual algorithms.

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참고문헌

  1. S. N. Kong, C.-S. Moo, Y.-P. Chen, and Y.-C. Hsieh, “Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries,” Applied Energy, Vol. 86, No. 9, pp. 1506-1511, Sep. 2009. https://doi.org/10.1016/j.apenergy.2008.11.021
  2. N. Yang and G. Li, “State of charge estimation for pulse discharge of a LiFePO4 battery by a revised Ah counting,” Electrochim Acta, Vol. 151, No. 1, pp. 63-71, Jan. 2015. https://doi.org/10.1016/j.electacta.2014.11.011
  3. S. Lee, J. Kim, J. Lee, and B. H. Cho, “State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge,” Journal of Power Sources, Vol. 185, No. 2, pp. 1367-1373, Dec. 2008. https://doi.org/10.1016/j.jpowsour.2008.08.103
  4. N. A. Windarko and J. Choi, "SOC estimation based on OCV for NiMH batteries using an improved takacs model," J. Power Electron., Vol. 10, No. 2, pp.181-186, Mar. 2010. https://doi.org/10.6113/JPE.2010.10.2.181
  5. P. Spagnol, S. Rossi, and S. M. Savaresi, "Kalman filter SoC estimation for Li-Ion batteries," in 2011 IEEE International Conference on Control Applications (CCA), pp. 587-592, Sep. 2011.
  6. G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. modeling and identification,” J. Power Sources, Vol. 134, No. 2, pp. 262-276, Aug. 2004. https://doi.org/10.1016/j.jpowsour.2004.02.032
  7. G. L. Plett, “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation,” Journal of Power Sources, Vol. 134, No. 2, pp. 277-292, Aug. 2004. https://doi.org/10.1016/j.jpowsour.2004.02.033
  8. N. A. Windarko and J. Choi, "LiPB battery SOC estimation using extended Kalman filter improved with variation of single dominant parameter," J. Power Electron., Vol. 12, No. 1, pp.40-48, Jan. 2012. https://doi.org/10.6113/JPE.2012.12.1.40
  9. G. L. Plett, “Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Introduction and state estimation,” J. Power Sources, Vol. 161, No. 2, pp. 1356-1368, Oct. 2006. https://doi.org/10.1016/j.jpowsour.2006.06.003
  10. W. He, N. Williard, C. Chen, and M. Pecht, “State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation,” International Journal of Electrical Power & Energy Systems, Vol. 62, No. 11, pp. 783-791, Nov. 2014. https://doi.org/10.1016/j.ijepes.2014.04.059
  11. X. Chen, W. Shen, Z. Cao, A. Kapoor, and I. Hijazin, “Adaptive gain sliding mode observer for state of charge estimation based on combined battery equivalent circuit model,” Computers & Chemical Engineering, Vol. 64, No. 18, pp. 114-123, Jun. 2014. https://doi.org/10.1016/j.compchemeng.2014.02.015
  12. F. Yang, Y. Xing, D. Wang, and K.-L. Tsui, "A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile," Applied Energy, Vol. 164, pp. 387-399, Feb. 2016. https://doi.org/10.1016/j.apenergy.2015.11.072
  13. J. Kim, S. Lee, and B. H. Cho, “Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/Capacity estimation and SOH estimation,” IEEE Trans. Power Electron., Vol. 27, No. 1, pp. 436-451, Jan. 2012. https://doi.org/10.1109/TPEL.2011.2158554
  14. S. Sepasi, R. Ghorbani, and B. Y. Liaw, “Improved extended Kalman filter for state of charge estimation of battery pack,” J. Power Sources, Vol. 255, No. 1, pp. 368-376, Jun. 2014. https://doi.org/10.1016/j.jpowsour.2013.12.093
  15. D. Li, J. Ouyang, H. Li, and J. Wan, "State of charge estimation for LiMn2O4, power battery based on strong tracking sigma point Kalman filter," J. Power Sources, Vol. 279, pp. 439-449, Apr. 2015. https://doi.org/10.1016/j.jpowsour.2015.01.002
  16. H. Gholizade-Narm and M. Charkhgard, “Lithium-ion battery state of charge estimation based on square-root unscented Kalman filter,” IET Power Electronics, Vol. 6, No. 9, pp. 1833-1841, Nov. 2013. https://doi.org/10.1049/iet-pel.2012.0706
  17. Q. Chen, J. Jiang, S. Liu, and C. Zhang, "A novel sliding mode observer for state of charge estimation of EV lithium batteries," J. Power Electron., Vol. 16, No. 3, pp.1131-1140, May. 2016. https://doi.org/10.6113/JPE.2016.16.3.1131
  18. M. Gao, Y. Liu, and Z. He, "Battery state of charge online estimation based on particle filter," in Image and Signal Processing (CISP), 2011 4th International Congress pp. 15-17, 2011.
  19. X. Liu, Z. Chen, C. Zhang, and J. Wu, "A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation," Applied Energy, Vol. 123, pp. 263-272, Jun. 2014. https://doi.org/10.1016/j.apenergy.2014.02.072
  20. L. Zheng, L. Zhang, J. Zhu, G. Wang, and J. Jiang, "Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model," Applied Energy, Vol. 180, pp. 424-434, Oct. 2016. https://doi.org/10.1016/j.apenergy.2016.08.016
  21. C. Lin, H. Mu, R. Xiong, and W. Shen, "A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm," Applied Energy, Vol. 166, pp. 76-83, Mar. 2016. https://doi.org/10.1016/j.apenergy.2016.01.010
  22. H. He, R. Xiong, H. Guo, and S. Li, "Comparison study on the battery models used for the energy management of batteries in electric vehicles," Energy Conversion and Management, Vol. 64, pp. 113-121, Dec. 2012. https://doi.org/10.1016/j.enconman.2012.04.014
  23. Y. He, X. T. Liu, C. B. Zhang, and Z. H. Chen, “A new model for State-of-Charge (SOC) estimation for highpower Li-ion batteries,” Applied Energy, Vol. 101, No. 1, pp. 808-814, Jan. 2013. https://doi.org/10.1016/j.apenergy.2012.08.031
  24. H. Rahimi-Eichi, F. Baronti, and M. Y. Chow, “Online adaptive parameter identification and state-of-charge coestimation for lithium-ion-polymer battery cells,” IEEE Trans. Ind. Electron., Vol. 61, No. 4, pp. 2053-2061, Apr. 2014. https://doi.org/10.1109/TIE.2013.2263774
  25. D. Zhou, A. Ravey, F. Gao, A. Miraoui, and K. Zhang, "On-line estimation of lithium polymer batteries state-ofcharge using particle filter based data fusion with multi-models approach," Industry Applications Society Meeting. IEEE, pp. 1-8, 2015.
  26. USABC electric vehicle Battery Test Procedures Manual. Revision 2, DOE/ID-10479, 1996.
  27. J. Kim, S. Lee, and B. Cho, “Discharging/charging voltage- temperature pattern recognition for improved SOC/ capacity estimation and SOH prediction at various temperatures,” J. Power Electron., Vol. 12, No. 1, pp. 1-9, Jan. 2012. https://doi.org/10.6113/JPE.2012.12.1.1
  28. C. Zou, C. Manzie, D. Nesic, A. G. Kallapur, "Multi-time- scale observer design for state-of-charge and state-of- health of a lithium-ion battery," J. Power Sources, Vol. 335, pp. 121-130, Oct. 2016. https://doi.org/10.1016/j.jpowsour.2016.10.040