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

Minella Bezha, Ryo Gondo and Naoto Nagaoka
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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

Energies, 2019, vol. 12, issue 5, 1-21

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.

Keywords: optimal frequency region; dual ANN; parameter estimation; rechargeable battery; internal impedance; EV (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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