2012
DOI: 10.1109/tmag.2011.2176135
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A Study on the Novel Coefficient Modeling for a Skewed Permanent Magnet and Overhang Structure for Optimal Design of Brushless DC Motor

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Cited by 25 publications
(15 citation statements)
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“…The abovementioned methods are suitable for the tolerance design of electronic system, since it is easy to estimate the cost of each component. And (2) methods based on indirect optimization of approximation models by using different kinds of optimization algorithms, including intelligent algorithms [26][27][28][29][30][31]. Although these methods may theoretically improve the robustness of the EMDs, they fail to effectively consider the impact of permanent magnet working point migration, such that the batch production consistency has not been greatly improved.…”
Section: Introductionmentioning
confidence: 99%
“…The abovementioned methods are suitable for the tolerance design of electronic system, since it is easy to estimate the cost of each component. And (2) methods based on indirect optimization of approximation models by using different kinds of optimization algorithms, including intelligent algorithms [26][27][28][29][30][31]. Although these methods may theoretically improve the robustness of the EMDs, they fail to effectively consider the impact of permanent magnet working point migration, such that the batch production consistency has not been greatly improved.…”
Section: Introductionmentioning
confidence: 99%
“…Rotor overhang is another design aspect, which serves to increase the torque performances of a SPM motor by utilising the free space resulting from the end winding of the stator. By designing the rotor axis length to be longer than the stator axis length, the performance of the SPM motor can be enhanced without an expansion of the motor space [23][24][25][26][27][28][29][30]. Distributedwinding motors are advantageous because they have higher endwindings and thus more free space than concentrated-winding motors [24].…”
Section: Introductionmentioning
confidence: 99%
“…Although the non-probability model improves the optimization effect, it neglects correlations of uncertainties. In order to further improve the optimization effect, scholars have proposed an improved uncertainty quantitative method [18][19][20][21][22], which solves correlations of uncertainties to some extent, but these methods are only effective in a certain distribution and for linear design parameters. In addition, it should be pointed out that these methods only consider uncertainties from the manufacturing process and operational environment, optimize the initial quality, and ignore the degradation progress.…”
Section: Introductionmentioning
confidence: 99%