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Modelling of lithium-ion battery for online energy management systems

Modelling of lithium-ion battery for online energy management systems

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This study presents a new equivalent lithium-ion (Li-ion) battery model for online energy management system. It has an equilibrium potential E and an equivalent internal resistance Rint. The equilibrium potential E is expressed as a function of state-of-charge (SOC), current and temperature. The equivalent internal resistance Rint includes R1 and R2. R1 is defined as the resistance, which can be formulated by the discharging current and temperature. R2 is defined as the resistance which is because of the change of temperature. The adaptive extended Kalman filter is employed to implement the online energy management system based on the proposed Li-ion battery model. The SOC is considered as the state variable for the charging or discharging process of the Li-ion battery. The covariance parameters of the processing noise and observation errors are updated adaptively. The SOC of the Li-ion battery can be predicted by the online measured voltage and current in the online energy management system. The effectiveness and robustness of the proposed Li-ion battery model is validated. Experimental results show that the estimated SOC is accurate for various operating conditions. A comparison between the proposed method and other SOC estimation methods is also shown in the experimental results and analysis section.

References

    1. 1)
      • Lipman, T.E., Ramos, R., Kammen, M.: `An assessment of battery and hydrogen energy storage systems integrated with wind energy resources in California', California Energy Commission Public Interest Energy Research (PIER) Program, Technical Report, 2005.
    2. 2)
      • Sinopoli, J.: Microgrid energy management framework. July 2009, [Online]. Available at: http://bit.ly/f98iSX.
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • Le, H.T., Nguyen, T.Q.: `Sizing energy storage systems for wind power firming: an analytical approach and a cost-benefit analysis', Power & Energy Soc. General Meeting, 2008.
    8. 8)
    9. 9)
    10. 10)
      • Venu, C., Riffonneau, Y., Bacha, S., Baghzouz, Y.: `Battery storage system sizing in distribution feeders with distributed photovoltaic systems', IEEE Bucharest, PowerTech, June 2009.
    11. 11)
    12. 12)
    13. 13)
      • Department of Energy, US: Battery and electric vehicle report. July 2010, [Online]. Available at: http://bit.ly/fGaZPB.
    14. 14)
    15. 15)
      • Tang, X., Mao, X., Lin, J., Koch, B.: `Li-ion battery parameter estimation for state of charge', American Control Conf. (ACC), 2011, July 2011, p. 941–946.
    16. 16)
    17. 17)
      • T.R. Crompton . (2000) Battery reference book/T.R. Crompton.
    18. 18)
    19. 19)
      • Zhu, C., Coleman, M., Hurley, W.: `State of charge determination in a lead-acid battery: combined emf estimation and ah-balance approach’. Proc', PESC, June 2004, 3, p. 1908–1914.
    20. 20)
    21. 21)
    22. 22)
      • Fennie, C., Reisner, D., Salkind, A., Singh, P.: `A fuzzy logic approach to state-of-charge determination in high performance batteries with applications to electric vehicles', Electric Vehicle Symp., October 1998, 15, pp. 293–300.
    23. 23)
      • Zhihang, C., Shiqi, Q., Masrur, M.A., Murphey, Y.L.: `Battery state of charge estimation based on a combined model of extended Kalman filter and neural networks', Int. Joint Conf. on Neural Networks (IJCNN), 2011, p. 2156–2163.
    24. 24)
    25. 25)
    26. 26)
    27. 27)
    28. 28)
    29. 29)
      • Ultralife Corporation: Ubi-2590 smbus (part no. ubbl10) battery specification. August 2009, [Online]. Available at: http://bit.ly/gLgVY0.
    30. 30)
    31. 31)
      • P.S. Maybeck . (1979) Stochastic models, estimation and control.
    32. 32)
    33. 33)
      • National Instruments: Ni 9225 – national instruments. August 2011, [Online]. Available at: http://bit.ly/rd4bcv.
    34. 34)
      • Andrea D.: ‘State of charge estimate with Li-ion batteries’, http://bit.ly/nE34wB, October 2009.
    35. 35)
      • Dai, H., Wei, X., Sun, Z.: `Online SOC estimation of high-power lithium-ion batteries used on HEVs', ICVES 2006, December 2006, p. 342–347.
    36. 36)
      • Ultralife Corporation: Ubi-2590 battery charger specification. August 2009, [Online]. Available at: http://bit.ly/qgxzcS.
    37. 37)
      • Zhiwei, H., Mingyu, G., Jie, X.: `EKF-Ah based state of charge online estimation for lithium-ion power battery', Int. Computational Intelligence and Security Conf., 2009, 1, p. 142–145.
    38. 38)
      • Chen, S.X.: `Modeling of lithium-ion battery for energy storage system simulation', Technical Report,, 2008, Nanyang Technological University.
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