Articles | Volume 14
https://doi.org/10.5194/ars-14-55-2016
https://doi.org/10.5194/ars-14-55-2016
28 Sep 2016
 | 28 Sep 2016

Advanced binary search pattern for impedance spectra classification for determining the state of charge of a lithium iron phosphate cell using a support vector machine

Patrick Jansen, Michael Vollnhals, Daniel Renner, David Vergossen, Werner John, and Jürgen Götze

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Impedance spectra classification for determining the state of charge on a lithium iron phosphate cell using a support vector machine
P. Jansen, D. Vergossen, D. Renner, W. John, and J. Götze
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Cited articles

Álvarez Antón, J. C., García Nieto, P. J., Viejo, C. B., and Vilán Vilán, J. A.: Support Vector Machines Used to Estimate the Battery State of Charge, IEEE T. Power Electr., 28, 5919–5926, 2013.
Codeca, F., Savaresi, S., and Rizzoni, G.: On battery State of Charge estimation: A new mixed algorithm, IEEE Intl. Conf. Contr., 102–107, 2008.
Cortes, C. and Vapnik, V.: Support-vector networks, Machine Learning, Kluwer Academic Publishers, Boston, 20, 273–297, 1995.
Cover, T. M. and Hart, P. E.: Nearest Neighbor Pattern Classification, IEEE T. Inform. Theory, vol. IT-13, No.1, 21–27, 1967.
Dudani S. A.: The Distance-Weighted k-Nearest-Neighbor Rule, IEEE T. Syst. Man. Cyb., vol. SMC-13, No.4, 325–327, 1976.
Short summary
Further improvements on the novel method for state of charge (SOC) determination of lithium iron phosphate (LFP) cells based on the impedance spectra classification are presented.