Improving the Beetle Swarm Algorithm for Multi-parameter Identification of Permanent Magnet Assisted Synchronous Reluctance Motor

Permanent magnet auxiliary synchronous reluctance motor is a kind of motor with high power density, high efficiency, simple structure and low cost, which can replace the expensive permanent magnet synchronous motor for electric vehicles. However, the electric vehicle is affected by temperature change, magnetic saturation and other factors during operation, which leads to nonlinear changes in motor parameters, resulting in deterioration of control performance. Therefore, an improved Beetle algorithm based on Levi's flight and Gaussian variation is proposed for motor multi-parameter identification. By collecting the information of motor voltage and angular velocity, the improved algorithm is used to identify the motor parameters in the identification model of permanent magnet assisted synchronous reluctance motor. The simulation results show that the proposed algorithm is faster, more stable and more accurate in the identification of electrical and mechanical parameters, and the identification error is less than 1%.