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Lithium-ion battery modeling and parameter identification based on fractional theory

Author

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  • Hu, Minghui
  • Li, Yunxiao
  • Li, Shuxian
  • Fu, Chunyun
  • Qin, Datong
  • Li, Zonghua

Abstract

To effectively use and manage lithium-ion batteries and accurately estimate battery states such as state of charge and state of health, battery models with good robustness, accuracy and low-complexity need to be established. So the models can be embedded in microprocessors and provide accurate results in real-time. Firstly, this paper analyzes the electrochemical impedance spectrogram of lithium-ion battery, and adopts impedance elements with fractional order characteristics such as constant phase element and Warburg element to improve the second-order RC integer equivalent circuit model based on the fractional calculus theory. Secondly, a fractional-order equivalent circuit model of lithium-ion battery is established, which can accurately describe the electrochemical processes such as charge transfer reaction, double-layer effect, mass transfer and diffusion of lithium-ion battery. Thirdly, based on the mixed-swarm-based cooperative particle swarm optimization, parameter identification of the fractional-order equivalent circuit model is conducted using the federal city driving schedule experimental data in the time domain. The simulation results show that the model has higher accuracy and better robustness against different driving conditions, different SOC ranges and different temperatures than the second-order RC equivalent circuit model. The SOC estimation accuracy based on the fractional-order equivalent circuit model of lithium-ion battery is validated.

Suggested Citation

  • Hu, Minghui & Li, Yunxiao & Li, Shuxian & Fu, Chunyun & Qin, Datong & Li, Zonghua, 2018. "Lithium-ion battery modeling and parameter identification based on fractional theory," Energy, Elsevier, vol. 165(PB), pages 153-163.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pb:p:153-163
    DOI: 10.1016/j.energy.2018.09.101
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    Citations

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    Cited by:

    1. Chen, Liping & Wu, Xiaobo & Lopes, António M. & Yin, Lisheng & Li, Penghua, 2022. "Adaptive state-of-charge estimation of lithium-ion batteries based on square-root unscented Kalman filter," Energy, Elsevier, vol. 252(C).
    2. Ji’ang Zhang & Ping Wang & Yushu Liu & Ze Cheng, 2021. "Variable-Order Equivalent Circuit Modeling and State of Charge Estimation of Lithium-Ion Battery Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 14(3), pages 1-20, February.
    3. Oliver Stark & Martin Pfeifer & Sören Hohmann, 2021. "Parameter and Order Identification of Fractional Systems with Application to a Lithium-Ion Battery," Mathematics, MDPI, vol. 9(14), pages 1-19, July.
    4. Guoqing Jin & Lan Li & Yidan Xu & Minghui Hu & Chunyun Fu & Datong Qin, 2020. "Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions," Energies, MDPI, vol. 13(7), pages 1-17, April.
    5. Turksoy, Arzu & Teke, Ahmet & Alkaya, Alkan, 2020. "A comprehensive overview of the dc-dc converter-based battery charge balancing methods in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    6. Vichard, L. & Ravey, A. & Venet, P. & Harel, F. & Pelissier, S. & Hissel, D., 2021. "A method to estimate battery SOH indicators based on vehicle operating data only," Energy, Elsevier, vol. 225(C).
    7. Xiao, Feiyu & Xing, Bobin & Kong, Lingzhao & Xia, Yong, 2021. "Impedance-based diagnosis of internal mechanical damage for large-format lithium-ion batteries," Energy, Elsevier, vol. 230(C).
    8. Ahmed Fathy & Dalia Yousri & Abdullah G. Alharbi & Mohammad Ali Abdelkareem, 2023. "A New Hybrid White Shark and Whale Optimization Approach for Estimating the Li-Ion Battery Model Parameters," Sustainability, MDPI, vol. 15(7), pages 1-22, March.
    9. Ning Chen & Xu Zhao & Jiayao Chen & Xiaodong Xu & Peng Zhang & Weihua Gui, 2022. "Design of a Non-Linear Observer for SOC of Lithium-Ion Battery Based on Neural Network," Energies, MDPI, vol. 15(10), pages 1-26, May.
    10. Adrian Chmielewski & Jakub Możaryn & Piotr Piórkowski & Krzysztof Bogdziński, 2018. "Comparison of NARX and Dual Polarization Models for Estimation of the VRLA Battery Charging/Discharging Dynamics in Pulse Cycle," Energies, MDPI, vol. 11(11), pages 1-28, November.
    11. Esfandyari, M.J. & Esfahanian, V. & Hairi Yazdi, M.R. & Nehzati, H. & Shekoofa, O., 2019. "A new approach to consider the influence of aging state on Lithium-ion battery state of power estimation for hybrid electric vehicle," Energy, Elsevier, vol. 176(C), pages 505-520.
    12. S. Tamilselvi & S. Gunasundari & N. Karuppiah & Abdul Razak RK & S. Madhusudan & Vikas Madhav Nagarajan & T. Sathish & Mohammed Zubair M. Shamim & C. Ahamed Saleel & Asif Afzal, 2021. "A Review on Battery Modelling Techniques," Sustainability, MDPI, vol. 13(18), pages 1-26, September.
    13. Aissa Benhammou & Mohammed Amine Hartani & Hamza Tedjini & Hegazy Rezk & Mujahed Al-Dhaifallah, 2023. "Improvement of Autonomy, Efficiency, and Stress of Fuel Cell Hybrid Electric Vehicle System Using Robust Controller," Sustainability, MDPI, vol. 15(7), pages 1-21, March.
    14. He, Xitian & Sun, Bingxiang & Zhang, Weige & Fan, Xinyuan & Su, Xiaojia & Ruan, Haijun, 2022. "Multi-time scale variable-order equivalent circuit model for virtual battery considering initial polarization condition of lithium-ion battery," Energy, Elsevier, vol. 244(PB).

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