This paper proposes a digital twin modeling method for permanent magnet synchronous motors (IPMSMs) based on an improved grey wolf optimization (IGWO) algorithm. This method eliminates the need for signal injection, additional sensors and other measuring equipment, and realizes the identification of key internal parameters of IPMSMs. The improved grey wolf optimization algorithm enhances the global search capability and reduces the probability of falling into local optima. The method first establishes a PMSM model driven by a three-phase inverter, and uses the fourth-order Runge-Kutta method to discretize and solve the model. Then, the improved grey wolf optimization algorithm is adopted to iteratively optimize the parameters in the established digital twin model, thereby realizing the estimation of multiple parameters. Finally, theoretical verification is carried out using Simulink simulation data of the interior permanent magnet synchronous motor.
Dalei et al. (Wed,) studied this question.