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State of charge estimation of a lithium-ion battery using robust non-linear observer approach

State of charge estimation of a lithium-ion battery using robust non-linear observer approach

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A robust non-linear observer is proposed to estimate the state of charge (SoC) of a lithium-ion battery by employing an electrical model of the battery. Considering the non-linear behaviour of the open circuit voltage versus SoC curve, a non-linear state space model is established. The modelling errors and uncertainties are compensated by the proposed non-linear observer resulting in robustness in the presence of these errors, which is the main feature of the proposed observer. The stability of the observer is proved by the Lyapunov criteria. The effectiveness of the proposed observer is verified by using the experimental test. The test results show that the proposed approach is effective and estimates the SoC with high accuracy. Additional experimental test verifies the robust performance of the proposed observer in the presence of the modelling errors and disturbances.

References

    1. 1)
      • 1. Chaturvedi, N.A., Klein, R., Christensen, J., et al: ‘Algorithms for advanced battery management systems: modelling, estimation, and control challenges for lithium-ion battery’, IEEE Control Syst. Mag., 2010, 30, (3), pp. 4968.
    2. 2)
      • 2. Lukic, S.M., Cao, J., Bansal, R.C., et al: ‘Energy storage systems for automotive applications’, IEEE Trans. Ind. Electron., 2008, 55, (6), pp. 22582267.
    3. 3)
      • 3. Tannahill, V.R., Sutanto, D., Muttaqi, K.M., et al: ‘Future vision for reduction of range anxiety by using an improved state of charge estimation algorithm for electric vehicle batteries implemented with low-cost microcontrollers’, IET Electr. Syst. Transp., 2015, 5, (1), pp. 2432.
    4. 4)
      • 4. Hu, Y., Yurkovich, S.: ‘Battery state of charge estimation in automotive applications using LPV techniques’. Proc. American Control Conf., Baltimore, MD, USA, 2010, pp. 50435049.
    5. 5)
      • 5. Leng, F., Cher, M.T., Yazami, R., et al: ‘A practical frame work of electrical based online state-of-charge estimation of lithium ion batteries’, J. Power Sources, 2014, 255, pp. 423430.
    6. 6)
      • 6. Pei, L., Lu, R., Zhu, C.: ‘Relaxation model of the open-circuit voltage for state-of-charge estimation in lithium-ion batteries’, IET Electr. Syst. Transp., 2013, 3, (4), pp. 112117.
    7. 7)
      • 7. Cheng, M.W., Lee, Y.S., Liu, M., et al: ‘State-of-charge estimation with aging effect and correction for lithium-ion battery’, IET Electr. Syst. Transp., 2015, 5, (2), pp. 7076.
    8. 8)
      • 8. Cuadras, A., Kanoun, O.: ‘Soc Li-ion battery monitoring with impedance spectroscopy’. 6th IEEE Int. Multi-Conf. Systems, Signals, and Device, Djerba, Tunisia, March 2009, 5, pp. 15.
    9. 9)
      • 9. Yatsui, M.W., Bai, H.: ‘Kalman filter based state-of-charge estimation for lithium-ion batteries in hybrid electric vehicles using pulse charging’. IEEE Vehicle Power Propulsion Conf., Chicago, IL, USA, September 2011, pp. 15.
    10. 10)
      • 10. Chen, S.X., Gooi, H.B., Xia, N., et al: ‘Modelling of lithium-ion battery for online energy management systems’, IET Electr. Syst. Transp., 2012, 2, (4), pp. 202210.
    11. 11)
      • 11. Tran, N.-T., Khan, A.B., Choi, W.: ‘State of charge and state of health estimation of AGM VRLA batteries by employing a dual extended kalman filter and an ARX model for online parameter estimation’, Energies, 2017, 10, (1), p. 137.
    12. 12)
      • 12. Xing, Y., He, W., Petch, M., et al: ‘State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures’, Appl. Energy, 2014, 113, pp. 106115.
    13. 13)
      • 13. Aung, H., Low, K.S.: ‘Temperature dependent state-of-charge estimation of lithium ion battery using dual spherical unscented Kalman filter’, IET Power Electron., 2015, 8, (10), pp. 20262033.
    14. 14)
      • 14. Kim, I.-S.: ‘The novel state of charge estimation method for lithium battery using sliding mode observer’, J. Power Sources, 2006, 163, (1), pp. 584590.
    15. 15)
      • 15. Plett, G.L.: ‘Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: part 1. Introduction and state estimation’, J. Power Sources, 2006, 161, pp. 13561368.
    16. 16)
      • 16. Charkhgard, M., Zarif, M.H.: ‘Design of adaptive H∞ filter for implementing on state-of-charge estimation based on battery state-of-charge-varying modelling’, IET Power Electron., 2015, 8, (10), pp. 18251833.
    17. 17)
      • 17. Zhang, Y., Zhang, C., Zhang, X.: ‘State-of-charge estimation of the lithium-ion battery system with time-varying parameter for hybrid electric vehicles’, IET Control Theory Appl., 2014, 8, (3), pp. 160167.
    18. 18)
      • 18. Zhang, F., Liu, G., Fang, L., et al: ‘Estimation of battery state of charge with H∞ observer: applied to a robot for inspecting power transmission lines’, IEEE Trans. Ind. Electron., 2012, 59, (2), pp. 10861095.
    19. 19)
      • 19. Rui-hao, L., Yu-kun, S., Xiao-fu, J.: ‘Battery state of charge estimation for electric vehicle based on neural network’. IEEE 3rd Int. Conf. Communication Software Network, Xi'an, China, May 2011, pp. 493496.
    20. 20)
      • 20. Wang, J.: ‘Estimation of dynamic battery SoC using fuzzy arithmetic and the realization on DSP’. Master dissertation, Jilin University, 2007.
    21. 21)
      • 21. Wu, T., Wang, M., Xiao, Q., et al: ‘The SoC estimation of power Li-ion battery based on ANFIS model’, Smart Grid Renew. Energy, 2012, 3, pp. 5155.
    22. 22)
      • 22. Chaoui, H., Ibe-Ekeocha, C.C., Gualous, H.: ‘Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks’, Electr. Power Syst. Res., 2017, 146, pp. 189197.
    23. 23)
      • 23. Chang, W.-Y.: ‘Estimation of the state of charge for a LFP battery using a hybrid method that combines a RBF neural network, an OLS algorithm and AGA’, Int. J. Electr. Power Energy Syst., 2013, 53, pp. 603611.
    24. 24)
      • 24. Charkhgard, M., Farrokhi, M.: ‘State-of-charge estimation for lithium-ion batteries using neural networks and EKF’, IEEE Trans. Ind. Electron., 2010, 57, pp. 41784187.
    25. 25)
      • 25. Singh, P., Vinjamuri, R., Wang, X.Q., et al: ‘Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators’, J. Power Sources, 2006, 162, pp. 829836.
    26. 26)
      • 26. Chaoui, H., Sicard, P.: ‘Accurate state of charge (SoC) estimation for batteries using a reduced-order observer’. Proc. IEEE Int. Conf. Ind. Technol., Auburn, AL, USA, 2011, pp. 3943.
    27. 27)
      • 27. Verbrugge, M., Tate, E.: ‘Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena’, J. Power Sources, 2004, 126, (1-2), pp. 236249.
    28. 28)
      • 28. Xia, B., Chen, C., Tian, Y., et al: ‘A novel method for state of charge estimation of lithium-ion batteries using a nonlinear observer’, J. Power Sources, 2014, 270, pp. 359366.
    29. 29)
      • 29. Lakkis, M.E., Sename, O., Corno, M., et al: ‘Combined battery SoC/SOH estimation using a nonlinear adaptive observer’. 14th Annual European Control Conf., Linz, Austria, July 2015.
    30. 30)
      • 30. Hu, Y., Yurkovich, S.: ‘Battery cell state-of-charge estimation using linear parameter varying system techniques’, J. Power Sources, 2012, 198, pp. 338350.
    31. 31)
      • 31. Gholizadeh, M., Salmasi, S.R.: ‘Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model’, IEEE Trans. Ind. Electron., 2014, 61, (3), pp. 13351344.
    32. 32)
      • 32. Chen, Q., Jiang, J., Ruan, H., et al: ‘Simply designed and universal sliding mode observer for the SoC estimation of lithium-ion batteries’, IET Power Electron., 2017, 10, (6), pp. 697705.
    33. 33)
      • 33. Chen, X., Shen, W., Cao, Z., et al: ‘A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles’, J. Power Sources, 2014, 246, pp. 667678.
    34. 34)
      • 34. Chen, M., Rincon-Mora, G.A.: ‘Accurate electrical battery model capable of predicting runtime and I-V performance’, IEEE Trans. Energy Convers., 2006, 21, (2), pp. 504511.
    35. 35)
      • 35. Searcoid, M.O.: ‘Metric spaces’, springer undergraduate mathematics series’ (Springer-Verlag, Berlin, New York, 2006).
    36. 36)
      • 36. Gholizadeh, M., Yazdizadeh, A., Rahmati, M., et al: ‘SOC estimation for a lithium-Ion battery by designing a nonlinear observer based on an equivalent circuit model’. 15th IEEE Int. Conf. Industrial Informatics, Emden, Germany, 2017.
    37. 37)
      • 37. Chen, M.-S., Chen, C.-C.: ‘Robust nonlinear observer for Lipschitz nonlinear systems subject to disturbances’, IEEE Trans. Autom. Control, 2007, 52, (12), pp. 23652369.
    38. 38)
      • 38. Arnold, W.F., Laub, A.J.: ‘Generalized eigenproblem algorithms and software for algebraic Riccati equations’, Proc. IEEE, 1984, 72, (12), pp. 17461754.
    39. 39)
      • 39. Spagnol, P., Rossi, S., Savaresi, S.M.: ‘Kalman filter SoC estimation for Li-ion batteries’. Proc. IEEE Int. Conf. Control Application, Denver, CO, USA, 2011, pp. 587592.
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