2014
DOI: 10.4028/www.scientific.net/msf.792.233
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Lumped-Parameter Network Thermal Analysis of Permanent Magnet Synchronous Motor

Abstract: This paper develops a model based on an original lumped-parameter network configuration for the thermal transient analysis of a permanent magnet synchronous motor (PMSM). The specific PMSM was designed and optimized for a demanding aerospace application. The validity of the proposed lumped-parameter model is verified by measurements carried out in a thermal chamber.

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Cited by 5 publications
(4 citation statements)
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“…Under the extreme mode of operation, torque and rotating speed are equal to 6.0 m N ⋅ and 6,000 rpm respectively. High temperature, samarium cobalt permanent magnets and non-overlapping, alternate-teethwound, fractional slot concentrated winding configuration were used [8]- [11]. Induction Motor.…”
Section: Description Of Pmm and Im Actuatorsmentioning
confidence: 99%
See 2 more Smart Citations
“…Under the extreme mode of operation, torque and rotating speed are equal to 6.0 m N ⋅ and 6,000 rpm respectively. High temperature, samarium cobalt permanent magnets and non-overlapping, alternate-teethwound, fractional slot concentrated winding configuration were used [8]- [11]. Induction Motor.…”
Section: Description Of Pmm and Im Actuatorsmentioning
confidence: 99%
“…The specific lumped-parameter networks were implemented using Matlab and the SimPowerSystems library of Simulink [8]. The values of the parameters of the elements of the two models were determined by analytical expressions taking into consideration the geometric and operational characteristics of the IM and PMM.…”
Section: Lumped-parameter Model For Thermal Transient Analysismentioning
confidence: 99%
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“…After that, many researchers followed up and built various thermal networks. For example, Yabiku et al built a 9-node network for one linear motor, Rostami et al built a 13-node network for one axial flux permanent magnet machine [14][15][16][17], and Aldo Boglietti et al established four different orders of LPTN to study the effect of order on temperature prediction [18]. Moreover, Mohamed et al built a 3D LPTN to describe the thermal behavior of a YASA motor [19].…”
Section: Introductionmentioning
confidence: 99%