永磁同步电机（PMSM）在新能源汽车等领域应用广泛，其精确控制大多需要依赖准确的电机参数。利用改进的鲸鱼优化算法（WOA）对BP神经网络初始权值、阈值进行优化，基于改进的BP神经网络提出了一种高精度的PMSM参数辨识方法，实现了对PMSM定子电阻、d轴电感、q轴电感以及磁链的参数辨识。仿真验证结果表明，相较于传统BP神经网络和传统WOA算法优化的BP神经网络方法，所提出的方法具有更高的辨识精度，4个参数的辨识误差均小于2%。在电机试验平台上进一步验证了所提出方法的有效性。

Permanent magnet synchronous motor (PMSM) is widely used in new energy vehicles and other fields, and its precise control mostly depends on accurate motor parameters. The improved whale optimization algorithm (WOA) is used to optimize the initial weights and thresholds of BP neural network. Based on the improved BP neural network, a high-precision PMSM parameter identification method is proposed, which realizes the parameter identification of PMSM stator resistance, d-axis inductance, q-axis inductance and flux linkage. The simulation results show that, compared with traditional BP neural network and BP neural network method optimized by traditional WOA algorithm, the proposed method has higher identification accuracy, and the identification errors of the four parameters are all less than 2%. The effectiveness of the method is further verified on the experimental platform.