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Design of a Non-Linear Observer for SOC of Lithium-Ion Battery Based on Neural Network

Author

Listed:
  • Ning Chen

    (School of Automation, Central South University, Changsha 410083, China)

  • Xu Zhao

    (School of Automation, Central South University, Changsha 410083, China)

  • Jiayao Chen

    (School of Automation, Central South University, Changsha 410083, China)

  • Xiaodong Xu

    (School of Automation, Central South University, Changsha 410083, China)

  • Peng Zhang

    (School of Automation, Central South University, Changsha 410083, China)

  • Weihua Gui

    (School of Automation, Central South University, Changsha 410083, China)

Abstract

This paper presents a method for use in estimating the state of charge (SOC) of lithium-ion batteries which is based on an electrochemical impedance equivalent circuit model with a controlled source. Considering that the open-circuit voltage of a battery varies with the SOC, an equivalent circuit model with a controlled source is proposed which the voltage source and current source interact with each other. On this basis, the radial basis function (RBF) neural network is adopted to estimate the uncertainty in the battery model online, and a non-linear observer based on the radial basis function of the RBF neural network is designed to estimate the SOC of batteries. It is proved that the SOC estimation error is ultimately bounded by Lyapunov stability analysis, and the error bound can be arbitrarily small. The high accuracy and validity of the non-linear observer based on the RBF neural network in SOC estimation are verified with experimental simulation results. The SOC estimation results of the extended Kalman filter (EKF) are compared with the proposed method. It improves convergence speed and accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3835-:d:821996
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    References listed on IDEAS

    as
    1. Wang, Yujie & Zhang, Chenbin & Chen, Zonghai, 2014. "A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries," Applied Energy, Elsevier, vol. 135(C), pages 81-87.
    2. Zhengxin, Jiang & Qin, Shi & Yujiang, Wei & Hanlin, Wei & Bingzhao, Gao & Lin, He, 2021. "An Immune Genetic Extended Kalman Particle Filter approach on state of charge estimation for lithium-ion battery," Energy, Elsevier, vol. 230(C).
    3. Gelareh Javid & Djaffar Ould Abdeslam & Michel Basset, 2021. "Adaptive Online State of Charge Estimation of EVs Lithium-Ion Batteries with Deep Recurrent Neural Networks," Energies, MDPI, vol. 14(3), pages 1-14, February.
    4. Xiaosong Hu & Fengchun Sun & Yuan Zou, 2010. "Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer," Energies, MDPI, vol. 3(9), pages 1-18, September.
    5. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
    6. 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.
    7. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    8. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
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