Synchronous Reluctance Motor Modelling and Analysis

The significant fluctuations in the price of rare earth magnetic materials have shifted research focus toward developing rare earth-free electric motors. The Synchronous Reluctance Motor (SynRM) emerges as a promising candidate, offering the potential to compete with Permanent Magnet Machines while maintaining the affordability, simplicity, and ease of maintenance seen in Induction Machines. This research aims to develop a fast and accurate model of the Synchronous Reluctance Motor to generate optimal designs that enhance its performance. The primary contribution of this work is the development of a generalized Magnetic Equivalent Circuit (MEC) model for Synchronous Reluctance Motors, applicable across various pole-slot combinations and rotor barrier numbers. This generalized model efficiently analyzes numerous machine topologies and refines candidate machine designs, reducing the number of high-fidelity finite element simulations required throughout the optimization process. Initially, a two-pole model of the Synchronous Reluctance Motor with a single barrier was developed, iterated for all rotor pole and stator teeth combinations. Air-gap permeance modeling was also investigated, accommodating the rotor’s rotational movement. The proposed model predicts machine performance indices from design parameters, achieving a 92% accuracy compared to finite element analysis but with significantly reduced computation time. Sensitivity analysis on various SynRM geometrical parameters identified high-sensitivity and low-sensitivity parameters, optimizing the former with finite element analysis and the latter using the MEC model. This targeted approach helps narrow the design space, reducing optimization time and complexity. Additionally, multi-objective optimization was conducted using a high-performance computer with an Intel Xeon W7-3445 processor, demonstrating significant efficiency improvements. Implementing the mathematical model resulted in optimization time reductions ranging from 50% to 70%, depending on the task. This highlights the MEC model's practicality for initial design evaluations before high-fidelity finite element analysis, providing a balanced approach to accuracy and computational efficiency, and advancing more efficient motor designs.<p></p>