Adaptive Model Predictive Current Control for Variable Reluctance Flux Controllable PM Motors Considering Drive Cycles

The interior permanent magnet synchronous motor (IPMSM) has become core drive systems of electric vehicles (EVs). The variable reluctance flux controllable permanent magnet (VRFCPM) motor which features high efficiency, high torque density, and flux controllable is a new kind of IPMSM. Due to the variable reluctance effect caused by the armature current, the control of the motors always suffers mismatch parameters, lower efficiency, and etc. To solve this problem, this article proposes an adaptive model predictive current control (AMPCC) strategy of the VRFCPM motor for EV application. It focuses on the design of the adaptive current predictive model under multiple drive cycles. According to Popov hyperstable theory, a parameter adaption law of the VRFCPM motor is deduced under different operating conditions, where the range of parameter identification is varied. In addition, a cost function with variable inductance is also constructed to improve the performance of VRFCPM motors in current tracking error and efficiency. Finally, a comparative experiment was conducted with the multiple saliency-ratio model predictive control (MPC). The results show that the proposed AMPCC significantly reduces the d-q axis current tracking error, reduces the harmonic content of three-phase current, improves system efficiency. It verifies that the strategy can effectively overcome the parameter mismatch problem caused by variable reluctance.