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Parameter Sensitivity Analysis for Fractional-Order Modeling of Lithium-Ion Batteries

Author

Listed:
  • Daming Zhou

    (School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China
    Institut de recherche sur les transports l'énergie et la société (IRTES), Fédération de recherche FCLAB CNRS 3539, Université de Technologie de Belfort-Montbéliard, Belfort 90010, France)

  • Ke Zhang

    (School of Astronautics, Northwestern Polytechnical University, Xi’an 710072, China)

  • Alexandre Ravey

    (Institut de recherche sur les transports l'énergie et la société (IRTES), Fédération de recherche FCLAB CNRS 3539, Université de Technologie de Belfort-Montbéliard, Belfort 90010, France)

  • Fei Gao

    (Institut de recherche sur les transports l'énergie et la société (IRTES), Fédération de recherche FCLAB CNRS 3539, Université de Technologie de Belfort-Montbéliard, Belfort 90010, France)

  • Abdellatif Miraoui

    (Institut de recherche sur les transports l'énergie et la société (IRTES), Fédération de recherche FCLAB CNRS 3539, Université de Technologie de Belfort-Montbéliard, Belfort 90010, France)

Abstract

This paper presents a novel-fractional-order lithium-ion battery model that is suitable for use in embedded applications. The proposed model uses fractional calculus with an improved Oustaloup approximation method to describe all the internal battery dynamic behaviors. The fractional-order model parameters, such as equivalent circuit component coefficients and fractional-order values, are identified by a genetic algorithm. A modeling parameters sensitivity study using the statistical Multi-Parameter Sensitivity Analysis (MPSA) method is then performed and discussed in detail. Through the analysis, the dynamic effects of parameters on the model output performance are obtained. It has been found out from the analysis that the fractional-order values and their corresponding internal dynamics have different degrees of impact on model outputs. Thus, they are considered as crucial parameters to accurately describe a battery’s dynamic voltage responses. To experimentally verify the accuracy of developed fractional-order model and evaluate its performance, the experimental tests are conducted with a hybrid pulse test and a dynamic stress test (DST) on two different types of lithium-ion batteries. The results demonstrate the accuracy and usefulness of the proposed fractional-order model on battery dynamic behavior prediction.

Suggested Citation

  • Daming Zhou & Ke Zhang & Alexandre Ravey & Fei Gao & Abdellatif Miraoui, 2016. "Parameter Sensitivity Analysis for Fractional-Order Modeling of Lithium-Ion Batteries," Energies, MDPI, vol. 9(3), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:3:p:123-:d:64335
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    References listed on IDEAS

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    1. Dai, Haifeng & Wei, Xuezhe & Sun, Zechang & Wang, Jiayuan & Gu, Weijun, 2012. "Online cell SOC estimation of Li-ion battery packs using a dual time-scale Kalman filtering for EV applications," Applied Energy, Elsevier, vol. 95(C), pages 227-237.
    2. Hongwen He & Rui Xiong & Jinxin Fan, 2011. "Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach," Energies, MDPI, vol. 4(4), pages 1-17, March.
    3. Fares, Robert L. & Webber, Michael E., 2014. "A flexible model for economic operational management of grid battery energy storage," Energy, Elsevier, vol. 78(C), pages 768-776.
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    Cited by:

    1. Mu, Hao & Xiong, Rui & Zheng, Hongfei & Chang, Yuhua & Chen, Zeyu, 2017. "A novel fractional order model based state-of-charge estimation method for lithium-ion battery," Applied Energy, Elsevier, vol. 207(C), pages 384-393.
    2. Ming Cai & Weijie Chen & Xiaojun Tan, 2017. "Battery State-Of-Charge Estimation Based on a Dual Unscented Kalman Filter and Fractional Variable-Order Model," Energies, MDPI, vol. 10(10), pages 1-16, October.
    3. Xin Lu & Hui Li & Jun Xu & Siyuan Chen & Ning Chen, 2018. "Rapid Estimation Method for State of Charge of Lithium-Ion Battery Based on Fractional Continual Variable Order Model," Energies, MDPI, vol. 11(4), pages 1-18, March.
    4. Qingxia Yang & Jun Xu & Binggang Cao & Xiuqing Li, 2017. "A simplified fractional order impedance model and parameter identification method for lithium-ion batteries," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-13, February.

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