Abstract：In order to solve the problem of parameter estimation in the high-performance control of squirrel

cage asynchronous motor，a method for joint parameter identification of asynchronous motors with dual models

based on improved whale algorithm was proposed. This method can effectively identify the stator resistance，the

rotor resistance，mutual inductance and leakage inductance. In order to improve the identification accuracy of the

algorithm，the nonlinear convergence factor was adopted，and the ideas of chaotic reverse learning，simulated

annealing and adaptive mutation perturbation were integrated to overcome the shortcomings of the whale

algorithm，which relied on the initial population，was easy to fall into local optimum，and had low convergence

accuracy. Moreover，combining the advantages of the two traditional motor models，an improved dual-model joint

identification was proposed，which further improves the accuracy of parameter identification. Based on this model，

the improved whale algorithm was compared with the other two algorithms for motor parameter identification，and

the experimental results show that the improved algorithm has high recognition accuracy，which proves the

feasibility of applying the algorithm to identify the parameters of the squirrel cage asynchronous motor，and is of

great significance for improving the control performance of the squirrel cage asynchronous motor.

      Key words：squirrel cage；asynchronous motor；improved dual-model；improved whale optimization algorithm（WOA）；parameter identification