Physical Parameter Estimation for a Two-Level VSI Three-Phase PMSM Electric Drivetrain

A methodology for computing the physical parameters of a two-level VSI three-phase PMSM drivetrain is described. The method uses a load test at constant speed, and lumps resistive voltage drop, core loss resistance, and inverter nonlinearity together. Motor inductance, flux offset, and drivetrain resistance are calculated for a group of setpoints. The method uses a constant speed load test with only current sensors and a position sensor, and overcomes the rank deficiency problem by including adjacent points in an overdetermined unconstrained least squares minimization problem. Results demonstrate satisfactory physical parameter estimation with a 15% increase in airgap torque estimation accuracy compared to nameplate while withstanding a robustness to ±20% speed errors and ±10% current space harmonics.

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Publication Details

Published
2025-03-16
DOI
https://doi.org/10.1109/apec48143.2025.10977308
Primary Topic
Electric Motor Design and Analysis
Type
article
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article

Physical Parameter Estimation for a Two-Level VSI Three-Phase PMSM Electric Drivetrain

Bernard Steyaert, Matthias Preindl, Ananda Tjakra Adisurja
Electric Motor Design and Analysis
article

Physical Parameter Estimation for a Two-Level VSI Three-Phase PMSM Electric Drivetrain

Bernard Steyaert, Matthias Preindl, Ananda Tjakra Adisurja
article en

Abstract

A methodology for computing the physical parameters of a two-level VSI three-phase PMSM drivetrain is described. The method uses a load test at constant speed, and lumps resistive voltage drop, core loss resistance, and inverter nonlinearity together. Motor inductance, flux offset, and drivetrain resistance are calculated for a group of setpoints. The method uses a constant speed load test with only current sensors and a position sensor, and overcomes the rank deficiency problem by including adjacent points in an overdetermined unconstrained least squares minimization problem. Results demonstrate satisfactory physical parameter estimation with a 15% increase in airgap torque estimation accuracy compared to nameplate while withstanding a robustness to ±20% speed errors and ±10% current space harmonics.

Columbia University (US)
Affordable and clean energy
Openalex Percentile: Top 93%
Electric Motor Design and Analysis
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