access icon free Design of adaptive H ∞ filter for implementing on state-of-charge estimation based on battery state-of-charge-varying modelling

© The Institution of Engineering and Technology
Received 05/07/2014, Accepted 31/03/2015, Revised 08/03/2015, Published 09/06/2015

This study suggests a new method for modelling lithium-ion battery types and state-of-charge (SOC) estimation using adaptive H∞ filter (AHF). First, a universal linear model with some free parameters is considered for dynamical behaviour of the battery. The battery voltage and SOC are used as states of the model. Then for every period in the charge/discharge process the free parameters of the model are identified. Each period of process is associated with a specific SOC value, hence the parameters can be regarded as functions of SOC in the entire process. The functions are determined based on polynomial approximation and least squares method. The proposed SOC-varying model is incorporated in AHF for SOC estimation. Moreover, a new method for adjusting the tuning parameters of the filter is suggested. The proposed method is verified by experimental tests on a lithium-ion battery and is compared with adaptive extended Kalman filter and square-root unscented Kalman filter

Inspec keywords: secondary cells; H∞ filters; adaptive Kalman filters; nonlinear filters

Other keywords: state-of-charge estimation; square-root unscented Kalman filter; universal linear model; battery dynamical behaviour; lithium-ion battery types; polynomial approximation; SOC-varying model; AHF; adaptive H∞ filter design; charge-discharge process; tuning parameter adjustment; adaptive extended Kalman filter; battery state-of-charge-varying modelling

Subjects: Filtering methods in signal processing; Secondary cells; Secondary cells

References

    1. 1)
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
    10. 10)
    11. 11)
      • 41. Maybeck: ‘Stochastic models, estimation, and control’ (Academic Press, 1982).
    12. 12)
    13. 13)
    14. 14)
      • 1. Crompton, T.R.: ‘Battery reference book’ (Newnes, Oxford, U.K., 2000).
    15. 15)
    16. 16)
    17. 17)
    18. 18)
    19. 19)
    20. 20)
    21. 21)
      • 40. Ioannou, P.A., Sun, J.: ‘Robust adaptive control’ (Prentice Hall, 1996).
    22. 22)
    23. 23)
    24. 24)
      • 6. Hua, C.C., Fang, Y.H., Li, P.H.: ‘Charge equalization for series-connected lifepo4 battery strings’, IET Power Electron., 2015, p. 9, DOI: 10.1049/iet-pel.2014.0567.
    25. 25)
      • 43. IEC 61960: ‘Secondary cells and batteries containing alkaline or other non-acid electrolytes-secondary lithium celles and batteries for portable applications’, 2012.
    26. 26)
    27. 27)
    28. 28)
      • 38. Simon, D.: ‘Optimal state estimation’ (Wiley, 2006).
    29. 29)
      • 39. Golub, G.H., Van Loan, C.F.: ‘Matrix Computations’ (Johns Hopkins University Press, 2013, 4th edn.).
    30. 30)
    31. 31)
    32. 32)
      • 44. Åström, K.J., Wittenmark, B.: ‘Adaptive control’ (Addison-Wesley, 1995, 2nd edn.).
    33. 33)
    34. 34)
    35. 35)
    36. 36)
    37. 37)
    38. 38)
    39. 39)
    40. 40)
    41. 41)
    42. 42)
    43. 43)
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