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Multi-objective optimization of dual-stator permanent magnet motor based on composite algorithm.

Xiaoguang Kong1, Zhuo Yang2

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|July 2, 2025
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Summary
This summary is machine-generated.

This study optimized dual-stator permanent magnet motors (DSPMMs) to reduce cogging torque and improve performance. Novel optimization methods significantly enhanced average torque and reduced ripple, validating their effectiveness.

Keywords:
Cogging torqueDual-stator permanent magnet motorGenetic algorithmTaguchi method

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Area of Science:

  • Electrical Engineering
  • Mechanical Engineering
  • Materials Science

Background:

  • Dual-stator permanent magnet motors (DSPMMs) offer high torque density and fast response, crucial for heavy machinery.
  • Large cogging torque is a significant challenge in DSPMM applications.
  • Existing optimization methods may not fully address multi-variable complexities and performance targets.

Purpose of the Study:

  • To propose and validate novel optimization strategies for DSPMMs.
  • To effectively weaken cogging torque in DSPMMs.
  • To enhance average torque and reduce torque ripple under operational constraints.

Main Methods:

  • Multi-variable combination scanning (MVCS) method for initial optimization of external unit parameters.
  • Sensitivity analysis of internal unit motor parameters (magnetic bridge height, conductive layer thickness, air-gap length, air barrier width).
  • Taguchi method for significant variables and Kriging-based genetic algorithm for non-significant variables, followed by Pareto front selection.

Main Results:

  • Significant reduction in cogging torque achieved through MVCS optimization.
  • Substantial enhancement in average torque and reduction in torque ripple.
  • Demonstrated improvement in overall motor performance metrics post-optimization.

Conclusions:

  • The proposed multi-variable optimization methods are effective for DSPMMs.
  • The combined approach addresses complex design challenges, improving motor efficiency and reliability.
  • Optimized DSPMMs show enhanced performance suitable for demanding industrial applications.