When particle swarm optimization (PSO) is used to identify the parameters of permanent magnet synchronous motor (PMSM), the movement of particles is not selective, which makes the algorithm easy to fall into the local optimum, and the accuracy is poor. The simulated annealing particle swarm optimization (SAPSO) improves the accuracy and evolution speed, but SAPSO has redundant iteration problems. To solve these problems, a motor parameter identification method based on fast backfire double anneal particle swarm optimization (FBDAPSO) is proposed. By reducing the optimization time and quickly tempering and annealing the "misunderstood" difference, the motor adjustable model and fitness function are designed, and the number of iterations is constantly reset to achieve the effect of online identification. Under different working conditions, simulated and experimental results show that the proposed method can quickly and accurately identify the four parameters of the motor’s stator, winding resistance, stator winding d-axis inductance, stator winding q-axis inductance and permanent magnet flux linkage at the same time, compared with the traditional method of parameter identification, and it has better accuracy, rapidity, and robustness.