An Improved Model Predictive Current Control of BLDC Motor With a Novel Adaptive Extended Kalman Filter–Based Back EMF Estimator and a New Commutation Duration Approach for Electrical Vehicle

Abstract

As a result of the increasing use of electric vehicles, ensuring high-performance speed and torque control of brushless direct current (BLDC) motors has become of great importance for energy efficiency. In order to prevent the torque ripple of the finite control set model predictive current control (FCS-MPCC), commutation moments are detected by Hall effect sensors in conventional methods. However, this method cannot exhibit a long-life structure because of physical strain damaging the sensors and electrical connections. In this study, commutation moments and durations are captured and determined with a new approach. Commutation moments are captured with zero crossing detectors and commutation durations are determined by using the position information obtained from the encoder. Moreover, three-phase back electromotive forces (EMFs) of the BLDC motor applied to FCS-MPCC to predict the stator phase currents are estimated with a novel adaptive extended Kalman filter (AEKF) which has the estimation capability without any speed sensor. Furthermore, another improvement is implemented in the calculation of the cost function of FCS-MPCC by taking into account the difference between the predicted and the reference torque of the BLDC motor different from the conventional MPCC methods. The proposed drive system is tested under different scenarios at various speeds under load torque, stator resistance, and leakage inductance variations in simulation. It is proven by simulation results that phase commutations can be achieved stably with the proposed phase commutation determination method. In addition, the simulation results show that the proposed novel AEKF estimator and the FCS-MPCC in which the cost function is calculated by regarding not only the current error but also the moment error have impressive prediction and control performance, respectively.