DOI QR코드

DOI QR Code

Adaptive State-of-Charge Estimation Method for an Aeronautical Lithium-ion Battery Pack Based on a Reduced Particle-unscented Kalman Filter

  • Wang, Shun-Li (School of Information Engineering and Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology) ;
  • Yu, Chun-Mei (School of Information Engineering and Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology) ;
  • Fernandez, Carlos (School of Pharmacy and Life Sciences, Robert Gordon University) ;
  • Chen, Ming-Jie (School of Information Engineering and Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology) ;
  • Li, Gui-Lin (School of Information Engineering and Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology) ;
  • Liu, Xiao-Han (School of Information Engineering and Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Southwest University of Science and Technology)
  • Received : 2017.11.03
  • Accepted : 2018.03.01
  • Published : 2018.07.20

Abstract

A reduced particle-unscented Kalman filter estimation method, along with a splice-equivalent circuit model, is proposed for the state-of-charge estimation of an aeronautical lithium-ion battery pack. The linearization treatment is not required in this method and only a few sigma data points are used, which reduce the computational requirement of state-of-charge estimation. This method also improves the estimation covariance properties by introducing the equilibrium parameter state of balance for the aeronautical lithium-ion battery pack. In addition, the estimation performance is validated by the experimental results. The proposed state-of-charge estimation method exhibits a root-mean-square error value of 1.42% and a mean error value of 4.96%. This method is insensitive to the parameter variation of the splice-equivalent circuit model, and thus, it plays an important role in the popularization and application of the aeronautical lithium-ion battery pack.

Keywords

References

  1. A. Bartlett, J. Marcick, S. Onori, G. Rizzoni, X. G. Yang, and T. Miller, "Electrochemical model-based state of charge and capacity estimation for a composite electrode lithium-ion battery," IEEE Trans. Contr. Syst. Technol., Vol. 24, No. 2, pp. 384-399, Mar. 2016. https://doi.org/10.1109/TCST.2015.2446947
  2. T. Bruen and J. Marco, "Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system," J. Power Sources, Vol. 310, pp. 91-101, Apr. 2016. https://doi.org/10.1016/j.jpowsour.2016.01.001
  3. Q. Y. Chen, J. C. Jiang, S. J. Liu, and C. P. 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
  4. X. P. Chen, W. X. Shen, M. X. Dai, Z. W. Cao, J. Jin, and A. Kapoor, "Robust adaptive sliding-mode observer using RBF neural network for lithium-ion battery state of charge estimation in electric vehicles," IEEE Trans. Veh. Technol., Vol. 65 No. 4, pp. 1936-1947, Apr. 2016. https://doi.org/10.1109/TVT.2015.2427659
  5. X. J. Dang, L. Yan, K. Xu, X. R. Wu, H. Jiang, and H. X. Sun, "Open-circuit voltage-based state of charge estimation of lithium-ion battery using dual neural network fusion battery model," Electrochimica Acta, Vol. 188, pp. 356-366, Jan. 2016. https://doi.org/10.1016/j.electacta.2015.12.001
  6. G. Z. Dong, J. W. Wei, C. B. Zhang, and Z. H. Chen, "Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method," Applied Energy, Vol. 162, pp. 163-171, Jan. 2016. https://doi.org/10.1016/j.apenergy.2015.10.092
  7. B. Fridholm, T. Wik, and M. Nilsson, "Robust recursive impedance estimation for automotive lithium-ion batteries," J. Power Sources, Vol. 304, pp. 33-41, Feb. 2016. https://doi.org/10.1016/j.jpowsour.2015.11.033
  8. S. L. Wang, C. Fernandez, M. J. Chen, L. Wang, and J. Su, "A novel safety anticipation estimation method for the aerial lithium-ion battery pack based on the real-time detection and filtering," J. Cleaner Production, Vol. 185, pp. 187-197, Jun. 2018. https://doi.org/10.1016/j.jclepro.2018.01.236
  9. M. Lewerenz, S. Kabitz, M. Knips, J. Munnix, J. Schmalstieg, A. Warnecke, and D. U. Sauer, "New method evaluating currents keeping the voltage constant for fast and highly resolved measurement of Arrhenius relation and capacity fade," J. Power Sources, Vol. 353, pp. 144-151, Jun. 2017. https://doi.org/10.1016/j.jpowsour.2017.03.136
  10. D. J. Lim, J. H. Ahn, D. H. Kim, and B. K. Lee, "A mixed SOC estimation algorithm with high accuracy in various driving patterns of EVs," J. Power Electronics, Vol. 16, No. 1, pp. 27-37, Jan. 2016. https://doi.org/10.6113/JPE.2016.16.1.27
  11. K. Lim, H. A. Bastawrous, V. H. Duong, K. W. See, P. Zhang, and S. X. Dou, "Fading Kalman filter-based realtime state of charge estimation in LiFePO4 battery-powered electric vehicles," Applied Energy, Vol. 169, pp. 40-48, May 2016. https://doi.org/10.1016/j.apenergy.2016.01.096
  12. C. Lin, H. Mu, R. Xiong, and W. X. Shen, "A novel multimodel 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
  13. C. Z. Liu, W. Q. Liu, L. Y. Wang, G. D. Hu, L. P. Ma, and B. Y. Ren, "A new method of modeling and state of charge estimation of the battery," J. Power Sources, Vol. 320, pp. 1-12, Jul. 2016. https://doi.org/10.1016/j.jpowsour.2016.03.112
  14. N. Lotfi, R. G. Landers, J. Li, and J. Park, "Reduced-order electrochemical model-based SOC observer with output model uncertainty estimation," IEEE Trans. Contr. Syst. Technol., Vol. 25, No. 4, pp. 1217-1230, Jul. 2017. https://doi.org/10.1109/TCST.2016.2598764
  15. Y. Manane and R. Yazami, "Accurate state of charge assessment of lithium-manganese dioxide primary batteries," J. Power Sources, Vol. 359, pp. 422-426, Aug. 2017. https://doi.org/10.1016/j.jpowsour.2017.05.065
  16. J. H. Meng, G. Z. Luo, and F. Gao, "Lithium polymer battery state-of-charge estimation based on adaptive unscented Kalman filter and support vector machine," IEEE Trans. Power Electronics, Vol. 31, No. 3, pp. 2226-2238, Mar. 2016. https://doi.org/10.1109/TPEL.2015.2439578
  17. S. Nejad, D. T. Gladwin, and D. A. Stone, "A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states," J. Power Sources, Vol. 316, pp. 183-196, Jun. 2016. https://doi.org/10.1016/j.jpowsour.2016.03.042
  18. C. H. Piao, Z. C. Li, S. Lu, Z. K. Jin, and C. Cho, "Analysis of real-time estimation method based on hidden markov models for battery system states of health," J. Power Electron., Vol. 16, No. 1, pp. 217-226, Jan. 2016. https://doi.org/10.6113/JPE.2016.16.1.217
  19. S. Sepasi, R. Ghorbani, and B. Y. Liaw, "Inline state of health estimation of lithium-ion batteries using state of charge calculation," J. Power Sources, Vol. 299, pp. 246-254, Dec. 2015. https://doi.org/10.1016/j.jpowsour.2015.08.091
  20. S. L. Wang, C. Fernandez, L. P. Shang, Z. F. Li, and H. F. Yuan, "An integrated online adaptive state of charge estimation approach of high-power lithium-ion battery packs," Trans. Inst. Meas. Contr., Vol. 40, No. 6, pp. 1892-1910, Apr. 2018. https://doi.org/10.1177/0142331217694681
  21. W. Shi, J. L. Wang, J. M. Zheng, J. C. Jiang, V. Viswanathan, and J. G. Zhang, "Influence of memory effect on the state-of-charge estimation of large format Li-ion batteries based on LiFePO4 cathode," J. Power Sources, Vol. 312, pp. 55-59, Apr. 2016. https://doi.org/10.1016/j.jpowsour.2016.02.041
  22. L. S. Su, J. B. Zhang, J. Huang, H. Ge, Z. Li, F. C. Xie, and B. Y. Liaw, "Path dependence of lithium ion cells aging under storage conditions," J. Power Sources, Vol. 315, pp. 35-46, May 2016. https://doi.org/10.1016/j.jpowsour.2016.03.043
  23. S. L. Wang, L. P. Shang, Z. F. Li, H. Deng, and J. C. Li, "Online dynamic equalization adjustment of high-power lithium-ion battery packs based on the state of balance estimation," Applied Energy, Vol. 166, pp. 44-58, Mar. 2016. https://doi.org/10.1016/j.apenergy.2016.01.013
  24. Y. J. Wang, D. Yang, X. Zhang, and Z. H. Chen, "Probability based remaining capacity estimation using data-driven and neural network model," J. Power Sources, Vol. 315, pp. 199-208, May. 2016. https://doi.org/10.1016/j.jpowsour.2016.03.054
  25. J. L. Xie, J. C. Ma, Y. D. Sun, and Z. L. Li, "Estimating the state-of-charge of lithium-ion batteries using an H-infinity observer with consideration of the hysteresis characteristic," J. Power Electron., Vol. 16, No. 2, pp. 643-653, Mar. 2016. https://doi.org/10.6113/JPE.2016.16.2.643
  26. J. Xu, B. H. Liu, X. Y. Wang, and D. Y. Hu, "Computational model of 18650 lithium-ion battery with coupled strain rate and SOC dependencies," Applied Energy, Vol. 172, pp. 180-189, Jun. 2016. https://doi.org/10.1016/j.apenergy.2016.03.108
  27. F. F. Yang, Y. J. 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
  28. H. Zhang, L. Zhao, and Y. Chen, "A lossy counting-based state of charge estimation method and its application to electric vehicles," Energies, Vol. 8, No. 12, pp. 13811-13828, Dec. 2015. https://doi.org/10.3390/en81212395
  29. J. L. Zhang, Y. J. Wei, and H. H. Qi, "State of charge estimation of LiFePO4 batteries based on online parameter identification," Applied Mathematical Modelling, Vol. 40, No. 11, pp. 6040-6050, Jun. 2016. https://doi.org/10.1016/j.apm.2016.01.047
  30. Z. L. Zhang, X. Cheng, Z. Y. Lu, and D. J. Gu, "SOC Estimation of lithium-ion batteries with AEKF and wavelet transform matrix," IEEE Trans. Power Electron., Vol. 32, No. 10, pp. 7626-7634, Oct. 2017. https://doi.org/10.1109/TPEL.2016.2636180
  31. L. Zhao, M. Y. Lin, and Y. Chen, "Least-squares based coulomb counting method and its application for state-of-charge (SOC) estimation in electric vehicles," Int. J. Energy Res., Vol. 40, No. 10, pp. 1389-1399, Aug. 2016. https://doi.org/10.1002/er.3530