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Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery

Author

Listed:
  • Xiangdong Sun

    (School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Jingrun Ji

    (School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Biying Ren

    (School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Chenxue Xie

    (School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China)

  • Dan Yan

    (School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China)

Abstract

With the popularity of electric vehicles, lithium-ion batteries as a power source are an important part of electric vehicles, and online identification of equivalent circuit model parameters of a lithium-ion battery has gradually become a focus of research. A second-order RC equivalent circuit model of a lithium-ion battery cell is modeled and analyzed in this paper. An adaptive expression of the variable forgetting factor is constructed. An adaptive forgetting factor recursive least square (AFFRLS) method for online identification of equivalent circuit model parameters is proposed. The equivalent circuit model parameters are identified online on the basis of the dynamic stress testing (DST) experiment. The online voltage prediction of the lithium-ion battery is carried out by using the identified circuit parameters. Taking the measurable actual terminal voltage of a single battery cell as a reference, by comparing the predicted battery terminal voltage with the actual measured terminal voltage, it is shown that the proposed AFFRLS algorithm is superior to the existing forgetting factor recursive least square (FFRLS) and variable forgetting factor recursive least square (VFFRLS) algorithms in accuracy and rapidity, which proves the feasibility and correctness of the proposed parameter identification algorithm.

Suggested Citation

  • Xiangdong Sun & Jingrun Ji & Biying Ren & Chenxue Xie & Dan Yan, 2019. "Adaptive Forgetting Factor Recursive Least Square Algorithm for Online Identification of Equivalent Circuit Model Parameters of a Lithium-Ion Battery," Energies, MDPI, vol. 12(12), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2242-:d:239163
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    Citations

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    Cited by:

    1. Zeeshan Ahmad Khan & Prashant Shrivastava & Syed Muhammad Amrr & Saad Mekhilef & Abdullah A. Algethami & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "A Comparative Study on Different Online State of Charge Estimation Algorithms for Lithium-Ion Batteries," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    2. Xin Lai & Ming Yuan & Xiaopeng Tang & Yi Yao & Jiahui Weng & Furong Gao & Weiguo Ma & Yuejiu Zheng, 2022. "Co-Estimation of State-of-Charge and State-of-Health for Lithium-Ion Batteries Considering Temperature and Ageing," Energies, MDPI, vol. 15(19), pages 1-20, October.
    3. Cheng, Xingqun & Liu, Xiaolong & Li, Xinxin & Yu, Quanqing, 2024. "An intelligent fusion estimation method for state of charge estimation of lithium-ion batteries," Energy, Elsevier, vol. 286(C).
    4. Deng Ma & Kai Gao & Yutao Mu & Ziqi Wei & Ronghua Du, 2022. "An Adaptive Tracking-Extended Kalman Filter for SOC Estimation of Batteries with Model Uncertainty and Sensor Error," Energies, MDPI, vol. 15(10), pages 1-18, May.
    5. Cynthia Thamires da Silva & Bruno Martin de Alcântara Dias & Rui Esteves Araújo & Eduardo Lorenzetti Pellini & Armando Antônio Maria Laganá, 2023. "Two-Outputs Nonlinear Grey Box Model for Lithium-Ion Batteries," Energies, MDPI, vol. 16(5), pages 1-15, February.
    6. Zheng Chen & Jiapeng Xiao & Xing Shu & Shiquan Shen & Jiangwei Shen & Yonggang Liu, 2020. "Model-Based Adaptive Joint Estimation of the State of Charge and Capacity for Lithium–Ion Batteries in Their Entire Lifespan," Energies, MDPI, vol. 13(6), pages 1-15, March.
    7. Luis Alberto Cantera-Cantera & Cristóbal Vargas-Jarillo & Sergio Isaí Palomino-Reséndiz & Yair Lozano-Hernández & Carlos Manuel Montelongo-Vázquez, 2022. "A Polynomial Fitting Problem: The Orthogonal Distances Method," Mathematics, MDPI, vol. 10(23), pages 1-17, December.
    8. 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.
    9. Li, Chao & Zeng, Kaidi & Li, Guanzheng & Chen, Peiyu & Li, Bin, 2023. "A novel fault diagnosis method for battery energy storage station based on differential current," Applied Energy, Elsevier, vol. 352(C).
    10. Park, Shina & Song, Youngbin & Kim, Sang Woo, 2024. "Simultaneous diagnosis of cell aging and internal short circuit faults in lithium-ion batteries using average leakage interval," Energy, Elsevier, vol. 290(C).

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