New formulation of Loss-Surface Model for accurate iron loss modeling at extreme flux density and flux variation: Experimental analysis and test on a high-speed PMSM
The Loss-Surface (LS) model, commonly used for the prediction of iron losses in electrical machines, is reformulated for better accuracy and reliability when it is extrapolated beyond the parametric identification region. First, the dynamic hysteresis behaviors of typical soft magnetic materials are...
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Veröffentlicht in: | Journal of magnetism and magnetic materials 2022-12, Vol.563, p.169935, Article 169935 |
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Sprache: | eng |
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Zusammenfassung: | The Loss-Surface (LS) model, commonly used for the prediction of iron losses in electrical machines, is reformulated for better accuracy and reliability when it is extrapolated beyond the parametric identification region. First, the dynamic hysteresis behaviors of typical soft magnetic materials are analyzed by both measurements and FE simulations. They are then described by the LS model using different mathematical formulations. The most relevant one, which requires 14 parameters to reproduce and extrapolate any loss surface, will be presented in this paper. The newly reformulated LS model is tested, in the last part, to estimate the iron losses of intrinsic measurements as well as of an electrical machine. In the first case, the results obtained of four different materials are generally within 5% error. And in the latter case of a high speed permanent magnet synchronous motor, by performing a study on the effects of manufacturing methods (punching and assemble) with respect to the iron losses, it is shown that the LS model provides an estimation with around 10% of deviation.
•New formulation of the Loss-Surface model for more accurate iron loss estimation.•Application of the Loss-surface model in electrical machines.•Dynamic loss surface fully depicted by the Loss Surface model.•Influence of manufacturing methods on performance of a PMSM.•Manufacturing methods lead to increased iron losses in electrical machines. |
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ISSN: | 0304-8853 1873-4766 |
DOI: | 10.1016/j.jmmm.2022.169935 |