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Energies 2016, 9(8), 590; doi:10.3390/en9080590

Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery

Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
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Academic Editor: Sheng S. Zhang
Received: 14 June 2016 / Revised: 11 July 2016 / Accepted: 19 July 2016 / Published: 27 July 2016
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Abstract

A fractional derivative system identification approach for modeling battery dynamics is presented in this paper, where fractional derivatives are applied to approximate non-linear dynamic behavior of a battery system. The least squares-based state-variable filter (LSSVF) method commonly used in the identification of continuous-time models is extended to allow the estimation of fractional derivative coefficents and parameters of the battery models by monitoring a charge/discharge demand signal and a power storage/delivery signal. In particular, the model is combined by individual fractional differential models (FDMs), where the parameters can be estimated by a least-squares algorithm. Based on experimental data, it is illustrated how the fractional derivative model can be utilized to predict the dynamics of the energy storage and delivery of a lithium iron phosphate battery (LiFePO 4 ) in real-time. The results indicate that a FDM can accurately capture the dynamics of the energy storage and delivery of the battery over a large operating range of the battery. It is also shown that the fractional derivative model exhibits improvements on prediction performance compared to standard integer derivative model, which in beneficial for a battery management system. View Full-Text
Keywords: fractional differential model (FDM); energy storage and delivery; system identification; battery management system (BMS); least squares-based state-variable filter (LSSVF) method fractional differential model (FDM); energy storage and delivery; system identification; battery management system (BMS); least squares-based state-variable filter (LSSVF) method
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Jiang, Y.; Zhao, X.; Valibeygi, A.; de Callafon, R.A. Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery. Energies 2016, 9, 590.

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