Advertisement

State of Charge Estimation for Lithium-Ion Battery Using Sigma-Point Kalman Filters Based on the Second Order Equivalent Circuit Model

  • Nguyen Vinh ThuyEmail author
  • Nguyen Van Chi
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 104)

Abstract

Today Lithium-Ion battery (LiB) is very widely used in vehicular applications including Hybrid-Electric Vehicle (HEV), Plug-in Hybrid-Electric Vehicle (PHEV), Extended-Range Electric Vehicle (E-REV), and Electric Vehicle (EV). The improved discharge and charge efficiency, the longer life span, high energy density and the ability to deep cycle while maintaining power are the typical advantages of LiB. The most significant feature of vehicular applications powered by LiB which needs to be considered is the process of charging and discharging suddenly concerning to acceleration and breaking. The state of charge (SoC) estimation is an important problem related to energy and power control in the operation process of electric vehicles. In this paper, the SoC estimation method of LiB based on Sigma-point Kalman filter (SPKF) is proposed. The results based on the real data indicate that using the second-order equivalent circuit model of LiB can increase the accuracy of the SoC estimation compared with methods using first-order model.

Keywords

Lithium-Ion batteries State of charge Sigma-point Kalman filter Battery management system The equivalent circuit model 

References

  1. 1.
    Andrea, D.: Battery management systems for large Lithium-Ion battery packs. Artech House, pp. 22–110 (2010)Google Scholar
  2. 2.
    Nishi, Y.: Lithium ion secondary batteries; past 10 years and the future. J. Power Sources 100(1–2), 101–106 (2001)CrossRefGoogle Scholar
  3. 3.
    Chang, W.-Y.: The state of charge estimating methods for battery: a review. ISRN Appl. Math., 7 (2013). Article ID 953792, http://dx.doi.org/10.1155/2013/953792
  4. 4.
    Bundy, K., Karlsson, M., Lindbergh, G., Lundqvist, A.: An electrochemical impedance spectroscopy method for prediction of the state of charge of a nickel-metal hydride battery at open circuit and during discharge. J. Power Sources 72, 118–125 (1998)CrossRefGoogle Scholar
  5. 5.
    Holger, B., Oliver, B., Stephan, B., de Doncker, R.W., Fricke, B., Hammouche, A., Linzen, D., Thele, M., Sauer, D.U.: Impedance measurements on lead–acid batteries for state-of-charge, state-of-health and cranking capability prognosis in electric and hybrid electric vehicles. J. Power Sources 144, 418–425 (2005)Google Scholar
  6. 6.
    Gregory, L.P.: Battery Management Systems, Volume I: Battery Modeling. Artech House (2015)Google Scholar
  7. 7.
    He, W., Williard, N., Chen, C., Pecht, M.: State of charge estimation for electric vehicle batteries using unscented kalman filtering. Microelectron. Reliab. 53, 840–847 (2013)CrossRefGoogle Scholar
  8. 8.
    Lee, J., Nam, O., Cho, B.H.: Li-ion battery SoC estimation method based on the reduced order extended Kalman filtering. J. Power Sources 174, 9–15 (2007)CrossRefGoogle Scholar
  9. 9.
    Zhang, C.P., Jiang, J.C., Zhang, W.G., Sharkh, S.M.: Estimation of state of charge of lithium-ion batteries used in HEV using robust extended Kalman filtering. Energies 5(4), 1098–1115 (2012)CrossRefGoogle Scholar
  10. 10.
    Ritter, B., Mora, E., Schlicht, T., Schild, A., Konigorski, U.: Adaptive sigma-point Kalman filtering for wind turbine state and process noise estimation. J. Phys. Conf. Ser. 1037 (2018). Control and MonitoringGoogle Scholar
  11. 11.
    Rudolph, V.M., Eric, W.: Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. OGI School of Science & Engineering Oregon, Health & Science University Beaverton, Oregon, 97006, USA (2003)Google Scholar
  12. 12.
    Gregory, L.P.: Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs. Part 1: Introduction and state estimation. J. Power Sources 161(2), 1356–1368 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Electrical Engineering FacultyThai Nguyen University of TechnologyThai NguyenVietnam
  2. 2.Institute of High-Technology Research and Development for IndustryThai Nguyen University of TechnologyThai NguyenVietnam

Personalised recommendations