Advertisement

FRA and EKF Based State of Charge Estimation of Zinc-Nickel Single Flow Batteries

  • Yihuan LiEmail author
  • Kang Li
  • Shawn Li
  • Yanxue Li
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 925)

Abstract

The reliable state of charge (SOC) estimation is indispensable for flow batteries to maintain the safe and reliable operation. The widely adopted Extended Kalman filter (EKF) algorithm is a model-based method, however, the uncertainties in battery model will cause large errors in SOC estimation. An accurate battery model is the essence to capture the behaviors of batteries. In this paper, a novel framework for the SOC estimation of Zinc-nickel flow batteries is proposed based on the fast recursive algorithm (FRA) and extended Kalman filter (EKF). The FRA is firstly used to determine the model structure and identify the model parameters. Due to merits of FRA, a compact and accurate model of flow battery is built. Then, the SOC is estimated using the EKF based on the proposed linear-in-the-parameter model. Experimental studies and resultant simulations manifest the modelling accuracy of the proposed SOC estimation framework.

Keywords

Flow battery State of charge (SOC) Fast recursive algorithm (FRA) Extended Kalman filter (EKF) 

Notes

Acknowledgments

YH. LI would like to thank the China Scholarship Council (CSC) for sponsoring her research. S. LI, YX. LI and K. LI would like to thank the Macao Science and Technology Development Fund (FDCT) s support with the project (111/2013/A3)-Flow Battery Storage System Study and Its Application in Power System and Dr. CK Wong to provide the data and experimental resource.

References

  1. 1.
    Li, Y.X., et al.: Modeling of novel single flow zinc-nickel battery for energy storage system. In: 2014 IEEE 9th Conference on Industrial Electronics and Applications (ICIEA), pp. 1621–1626. IEEE (2014)Google Scholar
  2. 2.
    Cheng, J., Zhang, L., Yang, Y.S., Wen, Y.H., Cao, G.P., Wang, X.D.: Preliminary study of single flow zinc-nickel battery. Electrochem. Commun. 9(11), 2639–2642 (2007)CrossRefGoogle Scholar
  3. 3.
    Zhang, H., Li, X., Zhang, J.: Redox Flow Batteries: Fundamentals and Applications. CRC Press (2017)Google Scholar
  4. 4.
    De Leon, C.P., Frías-Ferrer, A., González-García, J., Szánto, D., Walsh, F.C.: Redox flow cells for energy conversion. J. Power Sources 160(1), 716–732 (2006)CrossRefGoogle Scholar
  5. 5.
    Rahimi-Eichi, H., Ojha, U., Baronti, F., Chow, M.Y.: Battery management system: an overview of its application in the smart grid and electric vehicles. IEEE Ind. Electron. Mag. 7(2), 4–16 (2013)CrossRefGoogle Scholar
  6. 6.
    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 (2018)CrossRefGoogle Scholar
  7. 7.
    Li, K., Peng, J.X., Irwin, G.W.: A fast nonlinear model identification method. IEEE Trans. Autom. Control 50(8), 1211–1216 (2005)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gregory, L.P.: Extended kalman filtering for battery management systems of lipbbased hev battery packs, part 2: modeling and identification. J. Power Sources 134(2), 262–276 (2004)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Li, K., Peng, J.X., Bai, E.W.: A two-stage algorithm for identification of nonlinear dynamic systems. Automatica 42(7), 1189–1197 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gregory, L.P.: Extended kalman filtering for battery management systems of lipb-based hev battery packs: part 1. background. J. Power Sources 134(2), 252–261 (2004)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Lin, X., Qin, J.: Joint estimation of single flow zinc-nickle battery state and parameter using unscented kalman filter. IEEE Power Energy Soc. General Meet. (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.School of Electronic and Electric EngineeringUniversity of LeedsLeedsUK
  2. 2.State Grid Energy Conservation Service Co., LTDBeijingChina

Personalised recommendations