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Cluster Computing

, Volume 22, Supplement 3, pp 6009–6018 | Cite as

SOC estimation optimization method based on parameter modified particle Kalman Filter algorithm

  • Shouzhen Zhang
  • Changjun Xie
  • Chunnian Zeng
  • Shuhai QuanEmail author
Article

Abstract

Traditional Kalman Filter algorithm requires the system noise to be Gaussian distribution, but the power battery operating condition generally can not meet the requirement due to complexity and disturbance by the environment. However, the Particle Filter algorithm can adapt to various forms of system noise. In this work, the calculation process of the standard Particle Filter algorithm is improved based on the engineering characteristics of SOC estimation. In the calculation process, the key parameters including the total number of particles and the effective particle threshold are optimized and verified under FTP75 and NEDC conditions. The systematic error under different conditions is evaluated, based on the vehicle platform computing capacity, the proposed total number of particles is 1000, the effective particle threshold is 0.01. In this case, the SOC estimation accuracy can reach 1–2%, meeting the practical requirements.

Keywords

SOC estimation Particle Filter algorithm Optimization modified parameters Recommended parameters 

Notes

Acknowledgements

This research was supported by the National Natural Science Foundation of China (51477125), the Hubei Science Fund for Distinguished Young Scholars (2017CFA049), the Wuhan youth morning project (2016070204010155), and the Fundamental Research Funds for the Central Universities (WUT: 2017II40GX).

References

  1. 1.
    Lu, L., Han, X., Li, J., et al.: A review on the key issues for lithium-ion battery management in electric vehicles. J. Power Sources 226, 272–288 (2013)CrossRefGoogle Scholar
  2. 2.
    Waag, W., Fleischer, C., Sauer, D.U.: Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles. J. Power Sources 258, 321–339 (2014)CrossRefGoogle Scholar
  3. 3.
    Andre, D., Appel, C., Soczka-Guth, T., et al.: Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries. J. Power Sources 224, 20–27 (2013)CrossRefGoogle Scholar
  4. 4.
    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)CrossRefGoogle Scholar
  5. 5.
    Vasebi, A., Bathaee, S.M.T., Partovibakhsh, M.: Predicting state of charge of lead-acid batteries for hybrid electric vehicles by extended Kalman filter. Energy Convers. 49, 75–82 (2008)CrossRefGoogle Scholar
  6. 6.
    He, Z., Chen, D., Pan, C., Chen, L., et al.: State of charge estimation of power Li-ion batteries using a hybrid estimation algorithm based on UKF. Electrochim Acta 211, 101–109 (2016)CrossRefGoogle Scholar
  7. 7.
    Sun, F., Hu, X., Zou, Y., Li, S.: Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles. Energy 36, 3531–3540 (2011)CrossRefGoogle Scholar
  8. 8.
    KIM, K.H., JEE, G.I., PARK, C.G.: The stability analysis of the adaptive fading extended Kalman filter using the innovation covariance. Int. J. Control Autom. Syst. 7(1), 49–56 (2009)CrossRefGoogle Scholar
  9. 9.
    Li, Z., Huang, J., Zhang, J.: On state-of-charge determination for lithium-ion batteries. J. Power Sources 348, 281–301 (2017)CrossRefGoogle Scholar
  10. 10.
    Sepasi, S., Ghorbani, R., Liaw, B.Y.: Online state of health estimation of lithium-ion batteries using state of charge calculation. J. Power Sources 299, 246–254 (2015)CrossRefGoogle Scholar
  11. 11.
    Xiong, B., Zhao, J., Wei, Z.: Extended Kalman filter method for state of charge estimation of vanadium redox flow battery using thermal-dependent electrical model. J. Power Sources 262, 50–61 (2014)CrossRefGoogle Scholar
  12. 12.
    He, Y., Liu, X.T., Zhang, C.B., et al.: A new model for state-of-charge (SOC) estimation for high-power Li-ion batteries. Appl. Energy 101, 808–814 (2013)CrossRefGoogle Scholar
  13. 13.
    Xiong, R., Gong, X., Mi, C.C., et al.: A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter. J. Power Sources 243, 805–816 (2013)CrossRefGoogle Scholar
  14. 14.
    Ouyang, M.G., Zhang, M., Feng, X., et al.: Internal short circuit detection for battery pack using equivalent parameter and consistency method. J. Power Sources 294, 272–283 (2015)CrossRefGoogle Scholar
  15. 15.
    Sepasi, S., Ghorbani, R., Liaw, B.Y.: A novel on-board state-of-charge estimation method for aged Li-ion batteries based on model adaptive extended Kalman filter. J. Power Sources 245, 337–347 (2014)CrossRefGoogle Scholar
  16. 16.
    Zhang, S.M., Yang, L., Zhao, X.W., et al.: A GA optimization for lithium-ion battery equalization based on SOC estimation by NN and FLC. Electr. Power Energy Syst. 73, 318–328 (2015)CrossRefGoogle Scholar
  17. 17.
    Xie, C.J., Xu, X.Y., Piotr, B., et al.: Fuel cell and lithium iron phosphate battery hybrid powertrain with an ultracapacitor bank using direct parallel structure. J. Power Sources 279, 487–494 (2015)CrossRefGoogle Scholar
  18. 18.
    Weigert, T., Tian, Q., Lian, K.: State-of-charge prediction of batteries and battery-supercapacitor hybrids using artificial neural networks. J. Power Sources 196, 4061–4066 (2011)CrossRefGoogle Scholar
  19. 19.
    Tong, S., Lacap, J.H., Park, J.W.: Battery state of charge estimation using a load-classifying neural network. Energy Storage 7, 236–243 (2016)CrossRefGoogle Scholar
  20. 20.
    Singh, P., Fennie, C., Reisner, D.: Fuzzy logic modelling of state-of-charge and available capacity of nickel/metal hydride batteries. J. Power Sources 136, 322–333 (2004)CrossRefGoogle Scholar
  21. 21.
    Singh, P., Vinjamuri, R., Wang, X., Reisner, D.: Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators. J. Power Sources 162, 829–836 (2006)CrossRefGoogle Scholar
  22. 22.
    Hu, J., Lin, H., Li, X.: State-of-charge estimation for battery management system using optimized support vector machine for regression. J. Power Sources 269, 682–693 (2014)CrossRefGoogle Scholar
  23. 23.
    Meng, J., Luo, G., Gao, F.: Lithium polymer battery state-of-charge estimation based on adaptive unscented kalman filter and support vector machine. IEEE Trans. Power Electron. 31, 2226–2238 (2016)CrossRefGoogle Scholar
  24. 24.
    Sheng, H., Xiao, J., et al.: Electric vehicle state of charge estimation nonlinear correlation and fuzzy support vector machine. J. Power Sources 281, 131–137 (2015)CrossRefGoogle Scholar
  25. 25.
    Pathuri, B.V., Unterrieder, C., Huemer, M.: Battery internal state estimation: a comparative study of non-linear state estimation algorithms. In: Proceedings of the 2013 IEEE Vehicle Power and Propulsion Conference (VPPC), Beijing, China, 15–18, pp. 1–6 (October 2013)Google Scholar
  26. 26.
    Ye, M., Guo, H., Cao, B.: A model-based adaptive state of charge estimator for a lithium-ion battery using an improved adaptive particle filter. Appl. Energy 190, 740–748 (2017)CrossRefGoogle Scholar
  27. 27.
    Sbarufatti, C., Corbetta, M., Giglio, M., et al.: Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks. J. Power Sources 344, 128–140 (2017)CrossRefGoogle Scholar
  28. 28.
    Barbuto, M., Trotta, F., Bilotti, F., et al.: Filtering chiral particle for rotating the polarization state of antennas and waveguides components. IEEE Trans. Antennas Propag. 65(3), 1468–1471 (2017)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Sharifiana, M.S., Rahimib, A., Pariza, N.: Classifying the weights of particle filter in nonlinear systems. Commun. Nonlinear Sci. Numer. Simul. 44, 526–534 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Shouzhen Zhang
    • 1
    • 2
  • Changjun Xie
    • 2
  • Chunnian Zeng
    • 2
  • Shuhai Quan
    • 2
    Email author
  1. 1.School of Automotive EngineeringWuhan University of TechnologyWuhanChina
  2. 2.School of AutomationWuhan University of TechnologyWuhanChina

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