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Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation

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  • Wang, Yujie
  • Li, Mince
  • Chen, Zonghai

Abstract

In recent years, with the promotion of new energy vehicles and micro-grids, energy storage technology has achieved considerable development. The hybridization of the lithium-ion batteries with ultra-capacitors can effectively improve the power capability of the energy storage system, which is suitable for the vehicle and micro-grid applications. High precision modeling is the foundation of the state estimation and energy management of the energy storage system. The fractional-order model provides applicable computational complexity and accuracy. This paper presents the experimental study of the fractional-order models used for lithium-ion batteries and ultra-capacitors. In this work, the experimental results of the electrochemical impedance spectroscopy of both lithium-ion batteries and ultra-capacitors are systematically analyzed. Then the temperature compensating fractional-order models of the batteries and ultra-capacitors are developed. Moreover, the particle swarm optimization based global optimization is proposed for on-line parameter identification with strict constraints. Therefore the parameters of the fractional-order models can be extracted from daily vehicle operating conditions. Finally, the accuracy of the presented model is proved by dynamic cycle conditions at different temperatures. The results indicate that the proposed method can well approximate the voltage of both the lithium-ion batteries and the ultra-capacitors with mean relative errors less than 4% and 3%, respectively.

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  • Wang, Yujie & Li, Mince & Chen, Zonghai, 2020. "Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: Modeling, system identification, and validation," Applied Energy, Elsevier, vol. 278(C).
    • Handle: RePEc:eee:appene:v:278:y:2020:i:c:s0306261920312265
    DOI: 10.1016/j.apenergy.2020.115736
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    References listed on IDEAS

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    Cited by:

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    2. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    3. Gu, Yuxuan & Wang, Jianxiao & Chen, Yuanbo & Xiao, Wei & Deng, Zhongwei & Chen, Qixin, 2023. "A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control," Energy, Elsevier, vol. 264(C).
    4. Yang, Bowen & Wang, Dafang & Sun, Xu & Chen, Shiqin & Wang, Xingcheng, 2023. "Offline order recognition for state estimation of Lithium-ion battery using fractional order model," Applied Energy, Elsevier, vol. 341(C).
    5. Lin, Mingqiang & You, Yuqiang & Wang, Wei & Wu, Ji, 2023. "Battery health prognosis with gated recurrent unit neural networks and hidden Markov model considering uncertainty quantification," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    6. Peng Guo & Xiaobo Wu & António M. Lopes & Anyu Cheng & Yang Xu & Liping Chen, 2022. "Parameter Identification for Lithium-Ion Battery Based on Hybrid Genetic–Fractional Beetle Swarm Optimization Method," Mathematics, MDPI, vol. 10(17), pages 1-11, August.
    7. Feng, Fei & Yang, Rui & Meng, Jinhao & Xie, Yi & Zhang, Zhiguo & Chai, Yi & Mou, Lisha, 2022. "Electrochemical impedance characteristics at various conditions for commercial solid–liquid electrolyte lithium-ion batteries: Part. 2. Modeling and prediction," Energy, Elsevier, vol. 243(C).
    8. Brian Ospina Agudelo & Walter Zamboni & Eric Monmasson, 2021. "A Comparison of Time-Domain Implementation Methods for Fractional-Order Battery Impedance Models," Energies, MDPI, vol. 14(15), pages 1-23, July.
    9. Dong, Ao & Ma, Ruifei & Deng, Yelin, 2023. "Optimization on charging of the direct hybrid lithium-ion battery and supercapacitor for high power application through resistance balancing," Energy, Elsevier, vol. 273(C).
    10. Wang, Yujie & Zhang, Xingchen & Chen, Zonghai, 2022. "Low temperature preheating techniques for Lithium-ion batteries: Recent advances and future challenges," Applied Energy, Elsevier, vol. 313(C).

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