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An Estimation Model with Generalization Characteristics for the Internal Impedance of the Rechargeable Batteries by Means of Dual ANN Model

Author

Listed:
  • Minella Bezha

    (Doshisha University, Graduate School of Science and Engineering, Department of Electrical and Electronic, Engineering Power System Analysis Laboratory, 1-3 Tatara Miyakodani, Kyoto 610-0394, Japan)

  • Ryo Gondo

    (Doshisha University, Graduate School of Science and Engineering, Department of Electrical and Electronic, Engineering Power System Analysis Laboratory, 1-3 Tatara Miyakodani, Kyoto 610-0394, Japan)

  • Naoto Nagaoka

    (Doshisha University, Graduate School of Science and Engineering, Department of Electrical and Electronic, Engineering Power System Analysis Laboratory, 1-3 Tatara Miyakodani, Kyoto 610-0394, Japan)

Abstract

An estimation method of equivalent circuit parameters for rechargeable batteries that follows Artificial Neural Network (ANN) logic is proposed in this paper. The capability of the nonlinear analysis of the ANN is suitable for estimating the parameters that are nonlinearly involved in the complex circuit equation. The parameters have to be obtained from the complex internal impedances, which are measured in a wide frequency range. The accuracy is improved by dividing this wide range into a low-frequency and a high-frequency region. These regions are strongly related to the capacity fade and the maximum chargeable/dischargeable current, respectively. The improved method will determine the optimal frequency region for three different rechargeable batteries, which are composed of Li-Ion, Pb and Ni-MH. The accuracy of the proposed method is confirmed by a comparison with the measured results obtained using a conventional frequency domain method. For obtaining the real-time diagnostics of the battery, an improved dual ANN system, which employs unequal sampling, is proposed to obtain the circuit parameters. The deterioration of a battery can be detected from the estimated parameters, which can help in further investigations that aim to develop diagnostic models for the embedded circuit in industrial applications.

Suggested Citation

  • Minella Bezha & Ryo Gondo & Naoto Nagaoka, 2019. "An Estimation Model with Generalization Characteristics for the Internal Impedance of the Rechargeable Batteries by Means of Dual ANN Model," Energies, MDPI, vol. 12(5), pages 1-21, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:5:p:948-:d:213109
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    References listed on IDEAS

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    1. Qiao Zhu & Neng Xiong & Ming-Liang Yang & Rui-Sen Huang & Guang-Di Hu, 2017. "State of Charge Estimation for Lithium-Ion Battery Based on Nonlinear Observer: An H ∞ Method," Energies, MDPI, vol. 10(5), pages 1-19, May.
    2. Shichun Yang & Cheng Deng & Yulong Zhang & Yongling He, 2017. "State of Charge Estimation for Lithium-Ion Battery with a Temperature-Compensated Model," Energies, MDPI, vol. 10(10), pages 1-14, October.
    3. Cheng Siong Chin & Zuchang Gao, 2018. "State-of-Charge Estimation of Battery Pack under Varying Ambient Temperature Using an Adaptive Sequential Extreme Learning Machine," Energies, MDPI, vol. 11(4), pages 1-30, March.
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