A Separated-Frequency Parameters Identification Method of Li-Ion Battery Model for Electric Vehicles

Da Zhong Mu; Zhi Hong Dong; Ming Fei Wang

doi:10.4028/www.scientific.net/AMM.672-674.727

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 672-674A Separated-Frequency Parameters Identification...

A Separated-Frequency Parameters Identification Method of Li-Ion Battery Model for Electric Vehicles

Abstract:

This paper proposed a separated-frequency identification method of Li-ion battery model for Electric Vehicles (EVs). The main idea is to decompose the measured terminal voltage and current data in wavelet domain, and then the weighting least squares (LS) algorithm is used to extract the model parameters. Since the signal energy of open circuit voltage (OCV) mainly distributes in the low frequency band, the identifiable wavelet-domain battery model can be approximately obtained by neglecting the high frequency wavelet decomposition coefficients. Furthermore, based on the Akaike’s information criterion, we study the optimum decomposition order of the wavelet-domain battery model.

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Periodical:

Applied Mechanics and Materials (Volumes 672-674)

Pages:

727-730

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.672-674.727

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Online since:

October 2014

Authors:

Da Zhong Mu*, Zhi Hong Dong, Ming Fei Wang

Keywords:

Battery Model, Electric Vehicle (EV), Parameter Identification, Wavelet Decomposition

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References

[1] X. S. Hu, F. C. Sun Y. Zou and H. Peng, Online estimation of an electric vehicle lithm-ion battery using recursive least squares with forgetting, in Proc. of the 2011 American Control onference, San Francisco, USA, (2011).

DOI: 10.1109/acc.2011.5991260

Google Scholar

[2] M. Sitterly. L. Y. Wang , G.G. Yin, C. Wang, Enhanced Identification of Battery Models for Real-Time Battery Management, IEEE Trans. on Sustainable Energy, vol. 2, no. 3, pp: 300-308, (2011).

DOI: 10.1109/tste.2011.2116813

Google Scholar

[3] Y. H. Chiang, W. Y. Sean and J. C. Ke, Online estimation of internal resistance and open-circuit volatage for lithium-ion batteries in electric vehicles, Journal of Power Sources, vol. 196, no. 8, pp: 3921-3932, (2011).

DOI: 10.1016/j.jpowsour.2011.01.005

Google Scholar

[4] F.G. Meyer, Wavelet-based estimation of a semiparametric generalized linear model of fMRI time-series., IEEE Trans. Med. Imag. 2003, 22, 315–322.

DOI: 10.1109/tmi.2003.809587

Google Scholar

[5] Y. Hu, S. Uurkovich "Linear parameter varing battery model identification using subspace.

Google Scholar

[6] D. Z. Mu, J.C. Jiang and C.P. Zhang Online semiparametric identification of Lithium-Ion batteries using the wavelet-based partially linear battery model, Energies, vol. 6, no. 5, pp.2583-2604 , May. (2013).

DOI: 10.3390/en6052583

Google Scholar