2010
DOI: 10.3233/jae-2010-1169
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Design of novel magnetic flux barrier for reducing cogging torque in IPM type BLDC motor

Abstract: This paper introduces a rotor core in an interior permanent magnet type brushless DC motor. The rotor core consists of a rotor surface having a non-uniform air gap and a novel magnetic flux barrier. We show that, in the rotor core, the rotor surface having the non-uniform air gap effectively reduces the cogging torque while the optimized magnetic flux barrier reduces the cogging torque, and increases the average torque and the efficiency.

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Cited by 2 publications
(2 citation statements)
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“…For optimising, a V-shaped PMSM Taguchi's method was effective to increase average torque and reduce cogging torque [12][13][14], and also effective to improve efficiency and reduce cogging torque in a surface-mounted PMSM [15][16][17]. On the basis of Taguchi's method using signal-to-noise ratios (S/N) of control factors at different levels to determine the optimisation combination is a good method [18][19][20][21], and the noise factors was also taken into account to make the optimisation design more robust [22,23], but the control factors are selected artificially which has an effect on the optimisation accuracy. Another good way is combining Taguchi's method with other design methods such as fuzzy inference or neural network to obtain lower torque ripple factor and higher efficiency [24,25].…”
Section: Introductionmentioning
confidence: 99%
“…For optimising, a V-shaped PMSM Taguchi's method was effective to increase average torque and reduce cogging torque [12][13][14], and also effective to improve efficiency and reduce cogging torque in a surface-mounted PMSM [15][16][17]. On the basis of Taguchi's method using signal-to-noise ratios (S/N) of control factors at different levels to determine the optimisation combination is a good method [18][19][20][21], and the noise factors was also taken into account to make the optimisation design more robust [22,23], but the control factors are selected artificially which has an effect on the optimisation accuracy. Another good way is combining Taguchi's method with other design methods such as fuzzy inference or neural network to obtain lower torque ripple factor and higher efficiency [24,25].…”
Section: Introductionmentioning
confidence: 99%
“…The aforementioned research mainly utilized the characteristic of quick search for design space. On the basis of Taguchi's method using signal to noise ratios (S/N) of control factors at different levels to determine the optimization combination is a good method [21][22][23][24], and the noise factors was also taken into account to make the optimization design more robust [25,26]. Another good way is combining Taguchi's method with other design methods such as fuzzy inference or neural network to obtain lower torque ripple factor and higher efficiency [27,28].…”
Section: Introductionmentioning
confidence: 99%