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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Nguyen Thi Diep
Electric Power University
Control & Systems Engineering Electrical & Electronics Engineering

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Coupling coefficient observer based on Kalman filter for dynamic wireless charging systems Nguyen Kien Trung; Nguyen Thi Diep
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 1: March 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i1.pp337-347

Abstract

In the dynamic wireless charging system for electric vehicles, the transfer efficiency reaches the maximum value when the load impedance is equal to the optimal impedance value. However, the optimal impedance value depends on the coupling coefficient, which varies with the electric vehicle's position. Therefore, to track the optimal impedance as well as to improve the transfer efficiency, it is necessary to know the coupling coefficient. This paper proposes a coupling coefficient observer method based on the Kalman filter. Then, on the secondary side, an optimal impedance controller acts on the active rectifier to improve efficiency. The results show that the estimation method achieves high accuracy. The estimated error of the mutual inductance is less than 5% in both the case of impact measurement noise and change system parameters. System efficiency improved by 3.2% compared with the conventional estimation method.
Online parameter identification for equivalent circuit model of lithium-ion battery Nguyen Kien Trung; Nguyen Thi Diep
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp151-159

Abstract

Parameter identification is the most fundamental task for the model-based battery management system. However, there are some difficulties in completing this task since most of the existing methods require at least one known parameter or a time-consuming offline procedure to extract parameters from measured data. Based on the well-known thevenin equivalent circuit for battery, this paper determines the unique purpose is introducing the bounded varying forgetting factor recursive least square approach which identifies online all the parameters of the battery model at the same time. The precision of the proposed method is verified by simulation with the error converged to zero and the maximum error less than 1% of the nominal value.
Co-Authors Nguyen Kien Trung