A Comparative Study of Meta-Modeling Approaches for IPMSM Performance Prediction with Neural Networks

Improving an Interior Permanent Magnet Synchronous Machine (IPMSM) performance requires advanced approaches, often leading to an optimization problem. A Finite Element Method (FEM)-based numerical evaluation is needed to evaluate the accurate thermally stable output power of the IPMSM, which is computation-expensive. This paper presents and compares three computationally efficient meta-modeling approaches as alternatives to traditional FEM-based evaluation. All three approaches leverage Artificial Neural Networks (ANNs), with the highest accuracy achieved by using three deep ANNs for flux linkage and torque evaluation. These are combined with analytical models to estimate the output power. A multi-objective optimization scenario was conducted to demonstrate the advantages and limitations of the best-performing meta-modeling approach.