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Novel Parametric Circuit Modeling for Li-Ion Batteries

Author

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  • Ximing Cheng

    (Collaborative Innovation Center for Electric Vehicles in Beijing, National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
    Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Liguang Yao

    (Collaborative Innovation Center for Electric Vehicles in Beijing, National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Yinjiao Xing

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

  • Michael Pecht

    (Center for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD 20742, USA)

Abstract

Because of their simplicity and dynamic response, current pulse series are often used to extract parameters for equivalent electrical circuit modeling of Li-ion batteries. These models are then applied for performance simulation, state estimation, and thermal analysis in electric vehicles. However, these methods have two problems: The assumption of linear dependence of the matrix columns and negative parameters estimated from discrete-time equations and least-squares methods. In this paper, continuous-time equations are exploited to construct a linearly independent data matrix and parameterize the circuit model by the combination of non-negative least squares and genetic algorithm, which constrains the model parameters to be positive. Trigonometric functions are then developed to fit the parameter curves. The developed model parameterization methodology was applied and assessed by a standard driving cycle.

Suggested Citation

  • Ximing Cheng & Liguang Yao & Yinjiao Xing & Michael Pecht, 2016. "Novel Parametric Circuit Modeling for Li-Ion Batteries," Energies, MDPI, vol. 9(7), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:7:p:539-:d:73910
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    References listed on IDEAS

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    1. Bizhong Xia & Haiqing Wang & Yong Tian & Mingwang Wang & Wei Sun & Zhihui Xu, 2015. "State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter," Energies, MDPI, vol. 8(6), pages 1-21, June.
    2. Xing, Yinjiao & He, Wei & Pecht, Michael & Tsui, Kwok Leung, 2014. "State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures," Applied Energy, Elsevier, vol. 113(C), pages 106-115.
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    Cited by:

    1. 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.
    2. Gaizka Saldaña & José Ignacio San Martín & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Analysis of the Current Electric Battery Models for Electric Vehicle Simulation," Energies, MDPI, vol. 12(14), pages 1-27, July.
    3. Luke Farrier & Richard Bucknall, 2020. "Investigating the Performance Capability of a Lithium-ion Battery System When Powering Future Pulsed Loads," Energies, MDPI, vol. 13(6), pages 1-15, March.
    4. Wang, Qian-Kun & He, Yi-Jun & Shen, Jia-Ni & Ma, Zi-Feng & Zhong, Guo-Bin, 2017. "A unified modeling framework for lithium-ion batteries: An artificial neural network based thermal coupled equivalent circuit model approach," Energy, Elsevier, vol. 138(C), pages 118-132.
    5. Ana-Irina Stroe & Jinhao Meng & Daniel-Ioan Stroe & Maciej Świerczyński & Remus Teodorescu & Søren Knudsen Kær, 2018. "Influence of Battery Parametric Uncertainties on the State-of-Charge Estimation of Lithium Titanate Oxide-Based Batteries," Energies, MDPI, vol. 11(4), pages 1-19, March.

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