Articles | Volume 13
https://doi.org/10.5194/ars-13-127-2015
https://doi.org/10.5194/ars-13-127-2015
03 Nov 2015
 | 03 Nov 2015

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

Ávarez 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 Trans. Power Electro., 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, Dordrecht, the Netherlands, 20, 273–297, 1995.
Cover T. M. and Hart P. E.: Nearest Neighbor Pattern Classification, IEEE T. Inform. Theory, IT-13, 1, 21–27, 1967.
Dudani S. A.: The Distance-Weighted k-Nearest-Neighbor Rule, IEEE T Syst. Man. Cyb., SMC-13, 325–327, 1976.
Short summary
New method for determining the state of charge on lithium iron phosphate cells using frequency domain data.