Abstract: Aiming at the problems of long recognition time and slow convergence speed in the parameter identification process of permanent magnet synchronous motors of the basic particle swarm algorithm, this paper proposes a chaotic genetic particle swarm algorithm (CHPSO) combining chaotic mapping and information transmission to identify the parameters of permanent magnet synchronous motors online. The algorithm generates chaotic particles through chaos mapping, combines with the previous parameter identification results to generate an initialized population, and then introduces dynamic inertia weight coefficients to improve particle diversity. At the same time, step-by-step identification and cyclic updating methods are adopted to solve the problem of under-ranking parameter identification. The simulation shows that the deviations of the algorithm in identifying the motor parameters are 1.32% stator resistance, 1.08% flux linkage, 0.92% d-axis inductance and 1.16% q-axis inductance respectively. Finally, the effectiveness of the identification scheme is proved by bench experiments.