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A Lithium-Ion Battery Fractional Order State Space Model and its Time Domain System Identification

  • Hongjie Wu
  • Shifei Yuan
  • Chengliang Yin
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 192)

Abstract

This paper deals with a fractional order state space model for the lithium-ion battery and its time domain system identification method. Currently the equivalent circuit models are the most popular model which was frequently used to simulate the performance of the battery. But as we know, the equivalent circuit model is based on the integer differential equations, and the accuracy is limited. And the real processes are usually of fractional order as opposed to the ideal integral order models. So here we propose a lithium-ion battery fractional order state space model, and compare it with the equivalent circuit models, to see which model fit with the experiment results best. Then the hybrid pulse power characterization (HPPC) test has been implemented in the lithium-ion battery during varied state-of-charge (SOC). Based on the Levenberg–Marquardt algorithm, the parameters for each model have been obtained using the time-domain test data. Experimental results show that the proposed lithium-ion fractional order state space model has a better fitness than the classical equivalent circuit models. Meanwhile, five other cycles are adopt here to validate the prediction error of the two models, and final results indicate that the fractional model has better generalization ability.

Keywords

Fractional order state space model Equivalent circuit model Time domain system identification HPPC test Lithium-ion modelling 

Notes

Acknowledgments

This research work is supproted by CERC-CVC: U.S. - China Clean Energy Research Center Clean Vehicles Consortium (2010DFA72760-305). The Sinopoly Battery Ltd, a sponser of the battery cells for experimental test, is also gratefully acknowledged. And many thanks are also given to the authors whose papers were refered here.

References

  1. 1.
    Dees DW et al (2002) “Electrochemical modeling of lithium polymer batteries,” pp 310–320Google Scholar
  2. 2.
    Song L, Evans JW (2000) Electrochemical-thermal model of lithium polymer batteries. J Electrochem Soc 147:2086–2095CrossRefGoogle Scholar
  3. 3.
    Hellwig C et al (2011) “A multi-scale electrochemical and thermal model of a LiFePO battery”Google Scholar
  4. 4.
    Fang K et al (2012) A prediction model based on artificial neural network for surface temperature simulation of nickel-metal hydride battery during charging. J Power Sources 208:378–382CrossRefGoogle Scholar
  5. 5.
    Chan CC et al (2000) Available capacity computation model based on artificial neural network for lead-acid batteries in electric vehicles. J Power Sources 87:201–204CrossRefGoogle Scholar
  6. 6.
    Erdinc O et al (2009) A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system. J Power Sources 194:369–380CrossRefGoogle Scholar
  7. 7.
    Salkind AJ et al (1999) Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. J Power Sources 80:293–300CrossRefGoogle Scholar
  8. 8.
    Singh P et al (2006) Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators. J Power Sources 162:829–836CrossRefGoogle Scholar
  9. 9.
    “Equivalent circuit models”(2006) Motion Syst Des 48:42Google Scholar
  10. 10.
    Gomez J et al (2011) Equivalent circuit model parameters of a high-power Li-ion battery: thermal and state of charge effects. J Power Sources 196:4826–4831CrossRefGoogle Scholar
  11. 11.
    He H et al (2011) Evaluation of lithium-ion battery equivalent circuit models for state of charge estimation by an experimental approach. Energies 4:582–598CrossRefGoogle Scholar
  12. 12.
    Hu X et al (2011) “A comparative study of equivalent circuit models for Li-ion batteries.” J Power Sources 198(2012):359–367Google Scholar
  13. 13.
    Norian KH (2011) Equivalent circuit components of nickel-cadmium battery at different states of charge. J Power Sources 196:5205–5208CrossRefGoogle Scholar
  14. 14.
    Cugnet M et al (2009) “Fractional order model validation for the lead-acid battery resistance estimation: application to cranking capability.” In 7th IFAC international symposium on fault detection, supervision and safety of technical systems, SAFEPROCESS’09, 30 June 2009, 3 July 2009, Barcelona, Spain, pp 558–563Google Scholar
  15. 15.
    Podlubny I (1999) Fractional differential equations. Academic Press, New YorkGoogle Scholar
  16. 16.
    Sabatier J et al (2006) Fractional system identification for lead acid battery state of charge estimation. Signal Proc 86:2645–2657zbMATHCrossRefGoogle Scholar
  17. 17.
    Sabatier J et al (2010) A fractional order model for lead-acid battery crankability estimation. Commun Nonlinear Sci Numer Simul 15:1308–1317MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.National Engineering Laboratory for Automotive Electronic Control TechnologyShanghai Jiao Tong UniversityShanghaiChina

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