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Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter

Author

Listed:
  • Lei Zhang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China
    Faculty of Engineering and Information Technology, University of Technology, Sydney, Sydney 2007, Australia)

  • Zhenpo Wang

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • Fengchun Sun

    (National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China)

  • David G. Dorrell

    (Faculty of Engineering and Information Technology, University of Technology, Sydney, Sydney 2007, Australia)

Abstract

Ultracapacitors (UCs) are the focus of increasing attention in electric vehicle and renewable energy system applications due to their excellent performance in terms of power density, efficiency, and lifespan. Modeling and parameterization of UCs play an important role in model-based regulation and management for a reliable and safe operation. In this paper, an equivalent circuit model template composed of a bulk capacitor, a second-order capacitance-resistance network, and a series resistance, is employed to represent the dynamics of UCs. The extended Kalman Filter is then used to recursively estimate the model parameters in the Dynamic Stress Test (DST) on a specially established test rig. The DST loading profile is able to emulate the practical power sinking and sourcing of UCs in electric vehicles. In order to examine the accuracy of the identified model, a Hybrid Pulse Power Characterization test is carried out. The validation result demonstrates that the recursively calibrated model can precisely delineate the dynamic voltage behavior of UCs under the discrepant loading condition, and the online identification approach is thus capable of extracting the model parameters in a credible and robust manner.

Suggested Citation

  • Lei Zhang & Zhenpo Wang & Fengchun Sun & David G. Dorrell, 2014. "Online Parameter Identification of Ultracapacitor Models Using the Extended Kalman Filter," Energies, MDPI, vol. 7(5), pages 1-14, May.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:5:p:3204-3217:d:36129
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    References listed on IDEAS

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    2. Nayzel I. Jannif & Rahul R. Kumar & Ali Mohammadi & Giansalvo Cirrincione & Maurizio Cirrincione, 2023. "Constrained Least-Squares Parameter Estimation for a Double Layer Capacitor," Energies, MDPI, vol. 16(10), pages 1-19, May.
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    4. Zhang, Lei & Hu, Xiaosong & Wang, Zhenpo & Sun, Fengchun & Dorrell, David G., 2018. "A review of supercapacitor modeling, estimation, and applications: A control/management perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1868-1878.
    5. Kai Wang & Liwei Li & Huaixian Yin & Tiezhu Zhang & Wubo Wan, 2015. "Thermal Modelling Analysis of Spiral Wound Supercapacitor under Constant-Current Cycling," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-11, October.
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    8. Ahmad Yasin & Rached Dhaouadi & Shayok Mukhopadhyay, 2024. "A Novel Supercapacitor Model Parameters Identification Method Using Metaheuristic Gradient-Based Optimization Algorithms," Energies, MDPI, vol. 17(6), pages 1-31, March.
    9. Xiong, Rui & Cao, Jiayi & Yu, Quanqing, 2018. "Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 211(C), pages 538-548.
    10. Tae-Won Noh & Jung-Hoon Ahn & Byoung Kuk Lee, 2019. "Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries," Energies, MDPI, vol. 12(17), pages 1-14, September.
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