Sound quality (SQ) has become an important index to measure the competitiveness of motor products. To better evaluate and optimize SQ, a novelty SQ evaluation and prediction model of high-speed permanent magnet motor (HSPMM) with better accuracy is presented in this research. Six psychoacoustic parameters of A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, fluctuation strength (FS), and perferred-frequency speech interference (PSIL) were adopted to objectively evaluate the SQ of HSPMM under multiple operating conditions and subjective evaluation was also conducted by the combination of semantic subdivision method and grade scoring method. The evaluation results show that the SQ is poor, which will have a certain impact on human psychology and physiology. The correlation between the objective evaluation parameters and the subjective scores is analyzed by coupling the subjective and objective evaluation results. The average error of multiple linear regression (MLR) model is 7.10%. It has good accuracy, but poor stability. In order to improve prediction accuracy, a new predicted model of radial basis function (RBF) artificial neural network was put forward based on genetic algorithm (GA) optimization. Compared with MLR, its average error rate is reduced by 3.16% and the standard deviation is reduced by 1.841. In addition, the weight of each objective parameter was analyzed. The new predicted model has a better accuracy. It can evaluate and optimize the SQ exactly. The research methods and conclusions of this paper can be extended to the evaluation, prediction, and optimization of SQ of other motors.

声音质量（SQ）已成为衡量电机产品竞争力的重要指标。为了更好地评估和优化SQ，本研究提出了一种新颖且精度更高的高速永磁电机（HSPMM）SQ评估和预测模型。采用A计权声压级（ASPL）、响度、锐利度、粗糙度、波动强度（FS）和偏好频率语音干扰（PSIL）6个心理声学参数客观评价HSPMM在多种工作条件和主观条件下的SQ。还采用语义细分法和等级评分法相结合的方式进行评价。评估结果显示，SQ较差，会对人的心理、生理产生一定的影响。通过耦合主客观评价结果，分析客观评价参数与主观评分之间的相关性。多元线性回归（MLR）模型的平均误差为7.10%。其精度较好，但稳定性较差。为了提高预测精度，提出了一种基于遗传算法（GA）优化的径向基函数（RBF）人工神经网络预测模型。与MLR相比，其平均错误率降低了3.16%，标准差降低了1.841。此外，还分析了各客观参数的权重。新的预测模型具有更好的准确性。它可以准确地评估和优化SQ。本文的研究方法和结论可以推广到其他电机的SQ评估、预测和优化。