An Adaptive MTPA Control Method based on improved EKO for PMa-SynRM Drive System

The maximum torque per ampere (MTPA) control is usually used to achieve optimal control of energy efficiency of electric vehicles (EVs). However, the MTPA control strategy depends heavily on the motor model and parameters. The EVs are affected by temperature change, magnetic saturation and other factors during operation, which will lead to deterioration of MTPA control performance. Therefore, an adaptive MTPA control method based on improved extended Kalman observer (EKO) for permanent-magnet assisted synchronous reluctance motor (PMa-SynRM) drive system is proposed. By establishing the equivalent model of PMa-SynRM with iron loss, an improved extended Kalman filter algorithm based on d-axis current zeroing is proposed. This algorithm can estimate the iron loss and inductance parameters of the motor on the line, and the MTPA control parameters can be corrected in real time to optimize the stator current vector. The simulation results show that the proposed method has fast convergence speed, high parameter identification accuracy, and improves the MTPA control performance in case of parameter mismatch.