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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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    1. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    2. Maarten Messagie & Faycal-Siddikou Boureima & Thierry Coosemans & Cathy Macharis & Joeri Van Mierlo, 2014. "A Range-Based Vehicle Life Cycle Assessment Incorporating Variability in the Environmental Assessment of Different Vehicle Technologies and Fuels," Energies, MDPI, vol. 7(3), pages 1-16, March.
    3. Sun, Fengchun & Hu, Xiaosong & Zou, Yuan & Li, Siguang, 2011. "Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for electric vehicles," Energy, Elsevier, vol. 36(5), pages 3531-3540.
    4. Hong-Wen He & Rui Xiong & Yu-Hua Chang, 2010. "Dynamic Modeling and Simulation on a Hybrid Power System for Electric Vehicle Applications," Energies, MDPI, vol. 3(11), pages 1-10, November.
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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.
    3. 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.
    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.
    6. Zhilei Ge & Suyun Liu & Guopeng Li & Yan Huang & Yanni Wang, 2017. "Error model of geomagnetic-field measurement and extended Kalman-filter based compensation method," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-19, April.
    7. Kai Yit Kok & Parvathy Rajendran, 2016. "Differential-Evolution Control Parameter Optimization for Unmanned Aerial Vehicle Path Planning," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-12, March.
    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.
    11. Liu, Chunli & Li, Qiang & Wang, Kai, 2021. "State-of-charge estimation and remaining useful life prediction of supercapacitors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).

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