基于BP网络的电动汽车用无刷直流电机相角控制技术研究

无刷直流电机低速下存在电枢反应,影响电机出力;而高速下又需要弱磁控制,以拓展恒功率范围。因此,相角控制是至关重要的因素。BP神经网络具有强大的非线性映射能力,可以解决电流超前相角与转速、转矩之间的非线性关系。提出了基于BP网络的无刷直流电机相角控制技术,将实验数据作为训练样本进行离线训练,网络收敛后用作在线控制。实验结果表明,该方法可以使无刷直流电机及控制系统运行于高效区,满足电动汽车对驱动系统的要求。

Study on BP Neural Network Based Phase Control of Brushless DC Motor for Electric Vehicles

Armature reaction of bldcm in low speed influences torque-producing. In addition, field-weakening in high speed is necessary to ensure constant power operation of bldcm. So phase control is the most important factor to solve the above problems. The powerful nonlinear mapping function of BP neural network can settle the nonlinear relationship among phase advance, speed and torque. Thus the BP neural network based phase control method is presented, in which experimental data is applied to train BP neural network in off-line way and afterwards converged network to control on-line. The experimental results show that the bldcm and its control system based on the method run in high efficiency and satisfy the electric vehicle.