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Parameter Validation to Ascertain Voltage of Li-ion Battery Through Adaptive Control

  • Bhavani Sankar MalepatiEmail author
  • Deepak Vijay
  • Dipankar Deb
  • K. Manjunath
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 757)

Abstract

The objective of this paper is to ascertain the open- circuit voltage of a Lithium-ion battery in the presence of an uncertain parameter. The parameter relating to the state of charge (SoC) and open-circuit voltage are considered. An adaptive control law is developed to compensate for the uncertain parameter by considering its possible range of variation and estimating that parameter. Lyapunov stability analysis is carried out to ensure asymptotic stability and signal boundedness of the states associated with the system. The effectiveness of the methodology employed is verified by studying the dynamics of the battery in charging and discharging modes of operation. The robustness of the proposed adaptive controller is validated by considering the change in uncertain parameter to ensure asymptotic tracking.

Keywords

State of charge (SoC) Parameter uncertainty Adaptive control Model Reference Adaptive Control (MRAC) Li-ion battery 

References

  1. 1.
    Baloh, M., Tao, G., Allaire, P.: Adaptive estimation of magnetic bearing parameters. In: Control Applications, 1999. Proceedings of the 1999 IEEE International Conference on, vol. 2, pp. 1193–1198. IEEE (1999)Google Scholar
  2. 2.
    Cacciato, M., Nobile, G., Scarcella, G., Scelba, G.: Real-time model-based estimation of soc and soh for energy storage systems. IEEE Trans. Power Electron. 32(1), 794–803 (2017)CrossRefGoogle Scholar
  3. 3.
    Chen, M., Rincon-Mora, G.A.: Accurate electrical battery model capable of predicting runtime and iv performance. IEEE Trans. Energy Conversion 21(2), 504–511 (2006)CrossRefGoogle Scholar
  4. 4.
    Deb, D., Tao, G., Burkholder, J.O.: An adaptive inverse compensation scheme for signal-dependent actuator nonlinearities. In: Decision and Control, 2007 46th IEEE Conference on, pp. 4821–4826. IEEE (2007)Google Scholar
  5. 5.
    Deb, D., Tao, G., Burkholder, J.O., Smith, D.R.: Adaptive synthetic jet actuator compensation for a nonlinear tailless aircraft model at low angles of attack. In: American Control Conference, 2006, pp. 6–pp. IEEE (2006)Google Scholar
  6. 6.
    Deb, D., Tao, G., Burkholder, J.O., Smith, D.R.: Adaptive compensation control of synthetic jet actuator arrays for airfoil virtual shaping. J. Aircraft 44(2), 616–626 (2007)CrossRefGoogle Scholar
  7. 7.
    Giegerich, M., Koffel, S., Filimon, R., Grosch, J., Fuhner, T., Wenger, M., Gepp, M., Lorentz, V.: Electrothermal modeling and characterization of high capacity lithium-ion battery systems for mobile and stationary applications. In: Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE, pp. 6721–6727. IEEE (2013)Google Scholar
  8. 8.
    Gong, X., Xiong, R., Mi, C.C.: A data-driven bias-correction-method-based lithium-ion battery modeling approach for electric vehicle applications. IEEE Trans. Ind. Appl. 52(2), 1759–1765 (2016)Google Scholar
  9. 9.
    Gu, R., Malysz, P., Yang, H., Emadi, A.: On the suitability of electrochemical-based modeling for lithium-ion batteries. IEEE Trans. Trans. Electrification 2(4), 417–431 (2016)CrossRefGoogle Scholar
  10. 10.
    Joshi, S.M., Tao, G., Patre, P.: Direct adaptive control using an adaptive reference model. Int. J. Control 84(1), 180–196 (2011)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Kovvali, N., Banavar, M., Spanias, A.: An introduction to kalman filtering with matlab examples. Synth. Lectures Signal Process. 6(2), 1–81 (2013)CrossRefGoogle Scholar
  12. 12.
    Li, X., Choe, S.Y.: State-of-charge (soc) estimation based on a reduced order electrochemical thermal model and extended kalman filter. In: American Control Conference (ACC), 2013, pp. 1100–1105. IEEE (2013)Google Scholar
  13. 13.
    Li, Z., Hovakimyan, N.: L 1 adaptive controller for mimo systems with unmatched uncertainties using modified piecewise constant adaptation law. In: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on, pp. 7303–7308. IEEE (2012)Google Scholar
  14. 14.
    Li, Z., Soh, C.: Lyapunov stability of discontinuous dynamic systems. IMA J. Math. Control Inf. 16(3), 261–274 (1999)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Lin, X., Perez, H.E., Mohan, S., Siegel, J.B., Stefanopoulou, A.G., Ding, Y., Castanier, M.P.: A lumped-parameter electro-thermal model for cylindrical batteries. J. Power Sources 257, 1–11 (2014)CrossRefGoogle Scholar
  16. 16.
    Manjunath, K., Sarkar, V.: System level parameter tuning of an islanded microgrid with improved computational efficiency. In: Power and Energy Systems: Towards Sustainable Energy (PESTSE), 2016 Biennial International Conference on, pp. 1–7. IEEE (2016)Google Scholar
  17. 17.
    Parvini, Y., Vahidi, A., Fayazi, S.A.: Heuristic versus optimal charging of supercapacitors, lithium-ion, and lead-acid batteries: an efficiency point of view. IEEE Trans. Control Syst. Technol. 26(1), 167–180 (2018)CrossRefGoogle Scholar
  18. 18.
    Saxena, N., Hussain, I., Singh, B., Vyas, A.: Implementation of grid integrated pv-battery system for residential and electrical vehicle applications. IEEE Trans. Ind. Electron. (2017)Google Scholar
  19. 19.
    Tan, C., Tao, G., Qi, R., Yang, H.: A direct mrac based multivariable multiple-model switching control scheme. Automatica 84, 190–198 (2017)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Tao, G.: Inherent robustness of mrac schemes. Syst. Control Lett. 29(3), 165–173 (1996)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Underwood, S.J., Husain, I.: Online parameter estimation and adaptive control of permanent-magnet synchronous machines. IEEE Trans. Ind. Electron. 57(7), 2435–2443 (2010).  https://doi.org/10.1109/TIE.2009.2036029CrossRefGoogle Scholar
  22. 22.
    Wei, Z., Zou, C., Leng, F., Soong, B.H., Tseng, K.J.: Online model identification and state-of-charge estimate for lithium-ion battery with a recursive total least squares-based observer. IEEE Trans. Ind. Electron. 65(2), 1336–1346 (2018)CrossRefGoogle Scholar
  23. 23.
    Xu, G.Q., Yung, S.P.: Lyapunov stability of abstract nonlinear dynamic system in banach space. IMA J. Mathe. Control Inf. 20(1), 105–127 (2003)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Yang, J., Chen, Y., Yu, K.: State and unknown information estimation for non-linear systems with both input uncertainty and output disturbance. IMA J. Math. Control Inf. 33(2), 427–439 (2014)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Yao, L.W., Aziz, J., Kong, P.Y., Idris, N.: Modeling of lithium-ion battery using matlab/simulink. In: Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE, pp. 1729–1734. IEEE (2013)Google Scholar
  26. 26.
    Yu, Z., Huai, R., Xiao, L.: State-of-charge estimation for lithium-ion batteries using a kalman filter based on local linearization. Energies 8(8), 7854–7873 (2015)CrossRefGoogle Scholar
  27. 27.
    Zheng, L., Zhu, J., Wang, G., Lu, D.D.C., He, T.: Lithium-ion battery instantaneous available power prediction using surface lithium concentration of solid particles in a simplified electrochemical model. IEEE Trans. Power Electron. (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Bhavani Sankar Malepati
    • 1
    Email author
  • Deepak Vijay
    • 1
  • Dipankar Deb
    • 1
  • K. Manjunath
    • 1
  1. 1.Institute of Infrastructure Technology Research and ManagementAhmedabadIndia

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