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Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery

Author

Listed:
  • Zhenhua Cui
  • Jiyong Dai
  • Jianrui Sun
  • Dezhi Li
  • Licheng Wang
  • Kai Wang
  • A. M. Bastos Pereira

Abstract

With the increasing carbon emissions worldwide, lithium-ion batteries have become the main component of energy storage systems for clean energy due to their unique advantages. Accurate and reliable state-of-charge (SOC) estimation is a central factor in the widespread use of lithium-ion batteries. This review, therefore, examines the recent literature on estimating the SOC of lithium-ion batteries using the hybrid methods of neural networks combined with Kalman filtering (NN-KF), classifying the methods into Kalman filter-first and neural network-first methods. Then the hybrid methods are studied and discussed in terms of battery model, parameter identification, algorithm structure, implementation process, appropriate environment, advantages, disadvantages, and estimation errors. In addition, this review also gives corresponding recommendations for researchers in the battery field considering the existing problems.

Suggested Citation

  • Zhenhua Cui & Jiyong Dai & Jianrui Sun & Dezhi Li & Licheng Wang & Kai Wang & A. M. Bastos Pereira, 2022. "Hybrid Methods Using Neural Network and Kalman Filter for the State of Charge Estimation of Lithium-Ion Battery," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
    • Handle: RePEc:hin:jnlmpe:9616124
    DOI: 10.1155/2022/9616124
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    Citations

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    Cited by:

    1. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A combined state-of-charge estimation method for lithium-ion battery using an improved BGRU network and UKF," Energy, Elsevier, vol. 259(C).
    2. Ning Ma & Huaixian Yin & Kai Wang, 2023. "Prediction of the Remaining Useful Life of Supercapacitors at Different Temperatures Based on Improved Long Short-Term Memory," Energies, MDPI, vol. 16(14), pages 1-14, July.
    3. Xinwei Sun & Yang Zhang & Yongcheng Zhang & Licheng Wang & Kai Wang, 2023. "Summary of Health-State Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy," Energies, MDPI, vol. 16(15), pages 1-19, July.
    4. Dezhi Li & Dongfang Yang & Liwei Li & Licheng Wang & Kai Wang, 2022. "Electrochemical Impedance Spectroscopy Based on the State of Health Estimation for Lithium-Ion Batteries," Energies, MDPI, vol. 15(18), pages 1-26, September.
    5. Cui, Zhenhua & Kang, Le & Li, Liwei & Wang, Licheng & Wang, Kai, 2022. "A hybrid neural network model with improved input for state of charge estimation of lithium-ion battery at low temperatures," Renewable Energy, Elsevier, vol. 198(C), pages 1328-1340.
    6. Ping Ma & Shuhui Cui & Mingshuai Chen & Shengzhe Zhou & Kai Wang, 2023. "Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System," Energies, MDPI, vol. 16(15), pages 1-17, August.
    7. Khac Huan Su & Jaeyun Yim & Wonhee Kim & Youngwoo Lee, 2022. "Lyapunov-Based Controller Using Nonlinear Observer for Planar Motors," Mathematics, MDPI, vol. 10(13), pages 1-18, June.
    8. Zhang, Hao & Gao, Jingyi & Kang, Le & Zhang, Yi & Wang, Licheng & Wang, Kai, 2023. "State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network," Energy, Elsevier, vol. 283(C).
    9. Aleksander Suti & Gianpietro Di Rito & Giuseppe Mattei, 2022. "Development and Experimental Validation of Novel Thevenin-Based Hysteretic Models for Li-Po Battery Packs Employed in Fixed-Wing UAVs," Energies, MDPI, vol. 15(23), pages 1-26, December.
    10. Ming Zhang & Yanshuo Liu & Dezhi Li & Xiaoli Cui & Licheng Wang & Liwei Li & Kai Wang, 2023. "Electrochemical Impedance Spectroscopy: A New Chapter in the Fast and Accurate Estimation of the State of Health for Lithium-Ion Batteries," Energies, MDPI, vol. 16(4), pages 1-16, February.
    11. Juan D. Velásquez & Lorena Cadavid & Carlos J. Franco, 2023. "Intelligence Techniques in Sustainable Energy: Analysis of a Decade of Advances," Energies, MDPI, vol. 16(19), pages 1-45, October.
    12. Bingxiang Sun & Xianjie Qi & Donglin Song & Haijun Ruan, 2023. "Review of Low-Temperature Performance, Modeling and Heating for Lithium-Ion Batteries," Energies, MDPI, vol. 16(20), pages 1-37, October.
    13. Julan Chen & Guangheng Qi & Kai Wang, 2023. "Synergizing Machine Learning and the Aviation Sector in Lithium-Ion Battery Applications: A Review," Energies, MDPI, vol. 16(17), pages 1-22, August.

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