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

Author

Listed:
  • Yunfeng Jiang

    (Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA)

  • Xin Zhao

    (Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA)

  • Amir Valibeygi

    (Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA)

  • Raymond A. De Callafon

    (Department of Mechanical and Aerospace Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA)

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.

Suggested Citation

  • Yunfeng Jiang & Xin Zhao & Amir Valibeygi & Raymond A. De Callafon, 2016. "Dynamic Prediction of Power Storage and Delivery by Data-Based Fractional Differential Models of a Lithium Iron Phosphate Battery," Energies, MDPI, vol. 9(8), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:590-:d:74804
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    References listed on IDEAS

    as
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    Cited by:

    1. Jiang, Yunfeng & Xia, Bing & Zhao, Xin & Nguyen, Truong & Mi, Chris & de Callafon, Raymond A., 2017. "Data-based fractional differential models for non-linear dynamic modeling of a lithium-ion battery," Energy, Elsevier, vol. 135(C), pages 171-181.
    2. Yunfeng Jiang & Louis J. Shrinkle & Raymond A. de Callafon, 2019. "Autonomous Demand-Side Current Scheduling of Parallel Buck Regulated Battery Modules," Energies, MDPI, vol. 12(11), pages 1-20, May.
    3. Zhao, Xin & de Callafon, Raymond A., 2016. "Modeling of battery dynamics and hysteresis for power delivery prediction and SOC estimation," Applied Energy, Elsevier, vol. 180(C), pages 823-833.

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