{"title":"Lithium-Ion Battery State of Charge Estimation Using One State Hysteresis Model with Nonlinear Estimation Strategies","authors":"Mohammed Farag, Mina Attari, S. Andrew Gadsden, Saeid R. Habibi","volume":123,"journal":"International Journal of Materials and Metallurgical Engineering","pagesStart":237,"pagesEnd":242,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10006626","abstract":"Battery state of charge (SOC) estimation is an important
\r\nparameter as it measures the total amount of electrical energy stored
\r\nat a current time. The SOC percentage acts as a fuel gauge if it
\r\nis compared with a conventional vehicle. Estimating the SOC is,
\r\ntherefore, essential for monitoring the amount of useful life remaining
\r\nin the battery system. This paper looks at the implementation of three
\r\nnonlinear estimation strategies for Li-Ion battery SOC estimation.
\r\nOne of the most common behavioral battery models is the one
\r\nstate hysteresis (OSH) model. The extended Kalman filter (EKF),
\r\nthe smooth variable structure filter (SVSF), and the time-varying
\r\nsmoothing boundary layer SVSF are applied on this model, and the
\r\nresults are compared.","references":"[1] M. Farag, M. Fleckenstein, and S. Habibi, \u201cContinuous piecewise-linear,\r\nreduced-order electrochemical model for lithium-ion batteries in\r\nreal-time applications,\u201d Journal of Power Sources, vol. 342, pp. 351\u2013362,\r\nfeb 2017.\r\n[2] B. Bhangu, P. Bentley, D. Stone, and C. Bingham, \u201cNonlinear observers\r\nfor predicting state-of-charge and state-of-health of lead-acid batteries\r\nfor hybrid-electric vehicles,\u201d IEEE Trans. Veh. Technol., vol. 54, no. 3,\r\npp. 783\u2013794, may 2005.\r\n[3] A. Vasebi, S. Bathaee, and M. Partovibakhsh, \u201cPredicting state of charge\r\nof lead-acid batteries for hybrid electric vehicles by extended kalman\r\nfilter,\u201d Energy Conversion and Management, vol. 49, no. 1, pp. 75\u201382,\r\njan 2008.\r\n[4] T. Okoshi, K. Yamada, T. Hirasawa, and A. Emori, \u201cBattery condition\r\nmonitoring (BCM) technologies about lead\u2013acid batteries,\u201d Journal of\r\nPower Sources, vol. 158, no. 2, pp. 874\u2013878, aug 2006.\r\n[5] G. L. Plett, \u201cExtended kalman filtering for battery management systems\r\nof lipb-based hev battery packs: Part 1. background,\u201d Journal of Power\r\nsources, vol. 134, no. 2, pp. 252\u2013261, 2004.\r\n[6] \u201cExtended kalman filtering for battery management systems of\r\nlipb-based hev battery packs: Part 2. modeling and identification,\u201d\r\nJournal of power sources, vol. 134, no. 2, pp. 262\u2013276, 2004.\r\n[7] \u201cExtended kalman filtering for battery management systems of\r\nlipb-based hev battery packs: Part 3. state and parameter estimation,\u201d\r\nJournal of power sources, vol. 134, no. 2, pp. 277\u2013292, 2004.\r\n[8] M. Farag, S. Gadsden, S. Habibi, and J. Tjong, \u201cA comparative study of\r\nli-ion battery models and nonlinear dual estimation strategies,\u201d in 2012\r\nIEEE Transportation electrification conference and expo (ITEC). IEEE,\r\n2012, pp. 1\u20138.\r\n[9] M. Farag, M. Fleckenstein, and S. R. Habibi, \u201cLi-ion battery SOC\r\nestimation using non-linear estimation strategies based on equivalent\r\ncircuit models,\u201d in SAE Technical Paper Series. SAE International, apr\r\n2014.\r\n[10] X. Hu, S. Li, and H. Peng, \u201cA comparative study of equivalent circuit\r\nmodels for li-ion batteries,\u201d Journal of Power Sources, vol. 198, pp.\r\n359\u2013367, 2012.\r\n[11] J. Kim, S. Lee, and B. H. Cho, \u201cComplementary cooperation\r\nalgorithm based on DEKF combined with pattern recognition for\r\nSOC\/capacity estimation and SOH prediction,\u201d IEEE Transactions on\r\nPower Electronics, vol. 27, no. 1, pp. 436\u2013451, jan 2012.\r\n[12] B. D. O. Anderson, J. B. Moore, and M. Eslami, \u201cOptimal filtering,\u201d\r\nIEEE Transactions on Systems, Man, and Cybernetics, vol. 12, no. 2,\r\npp. 235\u2013236, 1982.\r\n[13] W. L. Brogan, \u201cApplied optimal estimation (arthur gels, ed.),\u201d SIAM\r\nRev., vol. 19, no. 1, pp. 172\u2013175, jan 1977.\r\n[14] M. S. Grewal and A. P. Andrews, Kalman Filtering: Theory and Practice\r\nwith MATLAB. JOHN WILEY & SONS INC, 2014. (Online). Available:\r\nhttp:\/\/www.ebook.de\/de\/product\/23151381\/mohinder s grewal angus\r\np andrews kalman filtering theory and practice with matlab.html\r\n[15] S. Habibi, \u201cThe smooth variable structure filter,\u201d Proceedings of the\r\nIEEE, vol. 95, no. 5, pp. 1026\u20131059, may 2007.\r\n[16] M. A. Al-Shabi, S. A. Gadsden, and S. R. Habibi, \u201cThe\r\ntoeplitz-observability smooth variable structure filter,\u201d in 2013 IEEE\r\nJordan Conference on Applied Electrical Engineering and Computing\r\nTechnologies (AEECT). Institute of Electrical & Electronics Engineers\r\n(IEEE), dec 2013.\r\n[17] S. A. Gadsden and S. R. Habibi, \u201cA new robust filtering strategy for\r\nlinear systems,\u201d J. Dyn. Sys., Meas., Control, vol. 135, no. 1, p. 014503,\r\noct 2012.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 123, 2017"}