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高效永磁同步磁阻电机设计与优化

Design and Optimization of High Efficiency Permanent Magnet Assisted Synchronous Reluctance Motor

作者:谷占起
  • 学号
    2019******
  • 学位
    硕士
  • 电子邮箱
    769******com
  • 答辩日期
    2022.05.20
  • 导师
    王善铭
  • 学科名
    电气工程
  • 页码
    77
  • 保密级别
    公开
  • 培养单位
    022 电机系
  • 中文关键词
    永磁辅助式同步磁阻电机, 凸极比, 参数化建模 , 多目标优化
  • 英文关键词
    Permanent magnet assisted synchronous reluctance motor, salient pole ratio, parametric modeling, multi-objective optimization

摘要

永磁辅助式同步磁阻电机(PMASynRM)结合了同步磁阻电机和永磁同步电机的特点,具有高效率、低成本、高转矩密度等优点,广泛应用在泵、风扇、空调压缩机和电动汽车等领域,其性能受到磁障形状、磁钢用量等多种因素的共同制约,各因素间的耦合和影响大大增加了电机设计难度,若设计不当可能会引起转矩脉动增加、振动噪声增大、效率降低等问题。因此,PMASynRM的设计及优化对于新能源电机的性能提高和降本增效具有重要意义。 采用解析计算法推导了PMASynRM中交直轴电感、磁阻转矩的解析表达式。仿真分析了极槽配合、绕组连接方式、节距、磁障形状、磁障厚度、磁障位置对电机凸极比的影响规律,为PMASynRM结构的设计提供了理论依据。 基于理论分析规律,设计和试制了12槽8极和24槽8极两台PMASynRM。仿真分析了两台电机的空载反电动势、齿槽转矩、最优电流控制角和效率云图,实验测量了电机的空载反电动势、额定工况下的电压电流波形、额定工况下电机达到热稳态的温升值,实验验证了本文初步设计方案的合理性。 为进一步提高反电动势正弦度、削弱转矩脉动,采用响应面算法对电机结构进行了优化。建立了两台电机的全参数化模型,采用随机抽样方法获得了样本集合,仿真分析了样本集中各电机方案的空载反电动势谐波幅值、齿槽转矩幅值、额定转矩纹波、齿上集中电磁力等指标与结构变量的相关性,研究了磁钢长宽、磁障张角、磁桥厚度等结构参数对以上指标的重要性程度,基于样本集的信息,采用移动最小二乘近似拟合得到了各指标的响应面模型,基于该模型,进行电机的多目标结构寻优,最终确定最优改进方案。通过结构优化前后的电机性能对比,验证了所提方案的有效性以及设计方法的准确性。

Permanent magnet assisted synchronous reluctance motor (PMASynRM) has the advantages of synchronous reluctance motor (SynRM) and permanent magnet synchronous motor (PMSM). PMASynRM has the advantages of high efficiency, low cost and high torque density, and has been increasingly applied in pumps, fans, air conditioning compressors and electric vehicles. The performance of PMASynRM is affected by many factors such as magnetic barrier shape, the size and arrangement of magnetic poles, the coupling and influence of each factor greatly increase the difficulty of motor design. Improper design may cause problems such as increased torque pulsation, increased vibration noise and reduced efficiency. Therefore, the design and optimization of PMASynRM is of great significance to the performance improvement, cost reduction and efficiency increase of PMASynRM. In this paper, the expressions of inductance and reluctance torque are derived by means of analytical method. The influence of slot number, pole number, winding connection mode, pitch, magnetic barrier shape, magnetic barrier thickness and magnetic barrier position on the electromagnetic performance of the motor is simulated and analyzed, which provides a theoretical basis for the design of PMASynRM structure. Based on theoretical analysis, two PMASynRM with 12S8P and 24S8P were designed and trial-produced. The no-load Back EMF, cogging torque, optimal current control Angle and efficiency of the two motors were simulated and analyzed. The no-load Back EMF of the motor, the voltage and current waveform under the rated working condition and the temperature rise of the motor under the rated working condition are measured experimentally, which verifies the rationality of the preliminary design scheme in this paper. In order to improve the sinusoidal degree of no-load Back EMF and reduce the torque ripple, response surface algorithm was used to optimize the motor structure. The parametric models of the two PMASynRMs were established, and the sample sets were obtained by random sampling method. The correlation between the no-load harmonic amplitude of Back EMF, the amplitude of cogging torque, the rated torque ripple, the electromagnetic force in the tooth and the design parameters of each motor was simulated and analyzed. The correlation of importance of design parameters such as length and width of magnetic steel, magnetic barrier Angle and magnetic bridge thickness to the responses above was studied. Based on the information of the sample set, the response surface model of each indicator was obtained by moving least square approximation fitting. Based on this model, the multi-objective structure optimization of the motor was carried out, and the optimal improvement scheme was finally determined. The effectiveness of the proposed scheme and the accuracy of the design method are verified by comparing the motor performance before and after the structure optimization.