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For high torque permanent magnet wheel motor, this paper describes an experimental research method to optimize and identify the motor parameters based on the results of offline calculation. In order to improve the accuracy of motor parameters identification, the motor model considering the affect of iron loss was established, and the motor parameters were identified using genetic algorithm (GA). Based on this, parameters validation experiment was performed. The results show that: parameters obtained by this method can be used to describe the steady-state and transient-state response of permanent magnet synchronous motors accurately.

Whether high-precision torque control or motor condition monitoring need accurate motor parameters. For the three parameters of surface-mounted permanent magnet synchronous motor (SPMSM), the voltage equation is rank-deficient. To solve this problem, some scholars proposed methods that build full rank equations with signal injection, but this will produce motor torque ripple, which is not suitable for application to the EPS. Therefore, this paper proposes a method based on MRAS to identify motor parameters step by step. The proposed two steps identification method can make the reference model full rank in every step, but the total decoupling between parameters identification processes cannot be realized for the assumption that the prior step result is the real value. It was found in experiment that this effect varies with the motor operating conditions.