Data-driven Modeling of High performance Interior Permanent Magnet Synchronous Motor using Dynamic Mode Decomposition and Uncertainty Quaitification

In order to fulfill the requirement of sensorless control drive of IPMSM in the electric vehicle, a new modeling technique for sensorless control the IPMSM is proposed. As the prediction by the physics-based model typically differ from the real measurements due to nonlinear nature of the model and inexact real-world effects, the model‘s accuracy is improved by using the measurement data to estimate the parameter under uncertainty quantification framework. Moreover, the data-driven modeling based on Koopman operator is proposed. The Koopman operator is an infinite dimensional linear operator which can describe the dynamics of observable nonlinear system. The finite-dimensional approximation of the Koopman operator is constructed by the Dynamic Mode Decomposition and its extension version. Hence, the nonlinear dynamic of the IPMSM can be expressed in a linear framework, to which the standard linear control approach can be nicely applied to control the IPMSM. The proposed models will be implemented in the model predictive control approach to study the feasibility.