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JRM Vol.28 No.5 pp. 633-639
doi: 10.20965/jrm.2016.p0633
(2016)

Paper:

Model-Based Minimum Total Loss Control of Interior Permanent Magnet Synchronous Motor

Gen-Sheng Li*,**, Zong-Xiao Yang*,**,†, Lei Song**, and Guan-Qiang Dong**

*School of Vehicle and Transportation Engineering, Henan University of Science and Technology
No.48, Xiyuan Road, Luoyang, Henan 471003, China

**Institute of Systems Science and Engineering, Henan University of Science and Technology
No.48, Xiyuan Road, Luoyang, Henan 471003, China

Corresponding author

Received:
February 16, 2016
Accepted:
June 20, 2016
Published:
October 20, 2016
Keywords:
PMSM, minimum total loss control, efficiency optimization, iron loss model
Abstract

Model-Based Minimum Total Loss Control of Interior Permanent Magnet Synchronous Motor

Speed impact on minimum total loss control

Increasing motor operating efficiency potentially reduces energy consumption and radiator requirements. In some applications, the motor’s operating efficiency may even be the most important factor to be considered. The model-based minimum total loss control (MTLC) we proposed could potentially improve motor efficiency by taking copper and iron loss into account based on the motor model, finding the minimum power point, and enabling the permanent magnet synchronous motor achieve optimal efficiency at different speeds. Copper loss under MTLC increased less than the maximum torque per ampere (MTPA), but iron loss under MTLC was much lower than MTPA, reducing total loss. MTLC reduced total loss by adjusting the relation between copper and iron loss using a demagnetization current. Analysis and simulation results show that our proposal improved the efficiency of the permanent magnet synchronous motor effectively, making it easy to implement in practical control with little calculation.

Cite this article as:
G. Li, Z. Yang, L. Song, and G. Dong, “Model-Based Minimum Total Loss Control of Interior Permanent Magnet Synchronous Motor,” J. Robot. Mechatron., Vol.28, No.5, pp. 633-639, 2016.
Data files:
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Last updated on Nov. 16, 2018