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A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries

Author

Listed:
  • Suwei Zhai

    (Electric Power Research Institute of China Southern Power Grid Yunnan Power Grid Co., Ltd., Kunming 650217, China)

  • Wenyun Li

    (Yunnan Power Dispatching Control Center of China Southern Power Grid, Kunming 650011, China)

  • Cheng Wang

    (College of IOT Engineering, Hohai University, Nanjing 210098, China)

  • Yundi Chu

    (College of IOT Engineering, Hohai University, Nanjing 210098, China)

Abstract

With the increasing proportion of Li-ion batteries in energy structures, studies on the estimation of the state of charge (SOC) of Li-ion batteries, which can effectively ensure the safety and stability of Li-ion batteries, have gained much attention. In this paper, a new data-driven method named the probabilistic threshold compensation fuzzy neural network (PTCFNN) is proposed to estimate the SOC of Li-ion batteries. Compared with other traditional methods that need to build complex battery models, the PTCFNN only needs data learning to obtain nonlinear mapping relationships inside Li-ion batteries. In order to avoid the local optimal value problem of traditional BP neural networks and the fixed reasoning mechanism of traditional fuzzy neural networks, the PTCFNN combines the advantages of a probabilistic fuzzy neural network and a compensation fuzzy neural network so as to improve the learning convergence speed and optimize the fuzzy reasoning mechanism. Finally, in order to verify the estimation performance of the PTCFNN, a 18650-20R Li-ion battery was used to carry out the estimation test. The results show that the mean absolute error and mean square error are very small under the conditions of a low-current test and dynamic-current test, and the overall estimation error is less than 1%, which further indicates that this method has good estimation ability.

Suggested Citation

  • Suwei Zhai & Wenyun Li & Cheng Wang & Yundi Chu, 2022. "A Novel Data-Driven Estimation Method for State-of-Charge Estimation of Li-Ion Batteries," Energies, MDPI, vol. 15(9), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3115-:d:801223
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    References listed on IDEAS

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    1. Hongyuan Yuan & Youjun Han & Yu Zhou & Zongke Chen & Juan Du & Hailong Pei, 2022. "State of Charge Dual Estimation of a Li-ion Battery Based on Variable Forgetting Factor Recursive Least Square and Multi-Innovation Unscented Kalman Filter Algorithm," Energies, MDPI, vol. 15(4), pages 1-22, February.
    2. Yinfeng Jiang & Wenxiang Song & Hao Zhu & Yun Zhu & Yongzhi Du & Huichun Yin, 2022. "Extended Rauch–Tung–Striebel Smoother for the State of Charge Estimation of Lithium-Ion Batteries Based on an Enhanced Circuit Model," Energies, MDPI, vol. 15(3), pages 1-17, January.
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    5. Yan Cheng & Xuesen Zhang & Xiaoqiang Wang & Jianhua Li, 2022. "Battery State of Charge Estimation Based on Composite Multiscale Wavelet Transform," Energies, MDPI, vol. 15(6), pages 1-16, March.
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    Cited by:

    1. Chuanping Wu & Yu Liu & Tiannian Zhou & Shiran Cao, 2022. "A Multistage Current Charging Method for Energy Storage Device of Microgrid Considering Energy Consumption and Capacity of Lithium Battery," Energies, MDPI, vol. 15(13), pages 1-15, June.

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