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IEEE Transactions on Industrial Electronics
Article . 2016 . Peer-reviewed
License: IEEE Copyright
Data sources: Crossref
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Iron Loss Analysis of the Permanent-Magnet Synchronous Machine Based on Finite-Element Analysis Over the Electrical Vehicle Drive Cycle

Authors: Janne Nerg; Vesa Ruuskanen; Juha Pyrhonen; Marko Rilla;
Abstract
Fast methods to estimate iron losses of the permanent magnet traction motor during the drive cycle of the electrical vehicle are compared. The methods use the iron loss information calculated by a finite-element analysis as a function of rotational speed both at no load and with a short-circuited stator to take into account the variable frequency and field weakening of the traction motor. The effect of iron losses on the optimal current components, providing the maximum efficiency, is studied. Several methods yield a good accuracy even based on the no-load iron loss only, but the accuracy can be improved especially in deep field weakening by including the short-circuit iron loss information in the analysis.
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citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).69 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%

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citations
Citations provided by BIP!
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
popularity
Popularity provided by BIP!
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
69
Top 1%
Top 10%
Top 10%