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<p>Achieving optimal performance in permanent magnet synchronous motors requires advanced optimization strategies after the initial design stage. Focusing on a direct-drive drilling permanent magnet synchronous motor (DPMSM), this paper proposes a novel hybrid multi-objective optimization approach integrating analytical modeling, response surface methodology (RSM), improved multi-objective particle swarm optimization (IMOPSO), and the Taguchi method. A magnetic field analytical model was first established based on 2-D polar coordinates and magnetic vector potential. Seven structural parameters were selected as optimization variables targeting increased average torque, reduced torque ripple, and lower copper loss. RSM was employed to construct surrogate models for significant variables identified through sensitivity analysis. An IMOPSO was then applied to derive the Pareto front. Subsequently, the Taguchi method was utilized to fine-tune non-significant variables based on the primary optimization results. Comparative analysis demonstrated improved operational performance of the optimized DPMSM. Experimental validation via a fabricated prototype confirmed the effectiveness and superiority of the proposed analytical model-RSM-IMOPSO-Taguchi method strategy.</p>

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Multi-Objective Optimization Strategy for DPMSM Employing Analytical Model, RSM, IMOPSO, and Taguchi Method

  • 期刊论文

  • 发表日期：

    2026年2月4日

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  • Lianpeng Mei,
  • Wensheng Xiao,
  • Qingxue Zhang,
  • Junguo Cui

摘要

Achieving optimal performance in permanent magnet synchronous motors requires advanced optimization strategies after the initial design stage. Focusing on a direct-drive drilling permanent magnet synchronous motor (DPMSM), this paper proposes a novel hybrid multi-objective optimization approach integrating analytical modeling, response surface methodology (RSM), improved multi-objective particle swarm optimization (IMOPSO), and the Taguchi method. A magnetic field analytical model was first established based on 2-D polar coordinates and magnetic vector potential. Seven structural parameters were selected as optimization variables targeting increased average torque, reduced torque ripple, and lower copper loss. RSM was employed to construct surrogate models for significant variables identified through sensitivity analysis. An IMOPSO was then applied to derive the Pareto front. Subsequently, the Taguchi method was utilized to fine-tune non-significant variables based on the primary optimization results. Comparative analysis demonstrated improved operational performance of the optimized DPMSM. Experimental validation via a fabricated prototype confirmed the effectiveness and superiority of the proposed analytical model-RSM-IMOPSO-Taguchi method strategy.