Integrated Bayesian Approach to FEM Parameter Estimation for Electric Motors

Abstract

The accurate calibration of Finite Element Models (FEM) for electric motors is critical to enhancing their performance, reliability, and design. This paper presents an integrated Bayesian approach for parameter estimation in FEM of electric motors. The Bayesian framework offers a robust and systematic method to update the uncertainty in model parameters based on experimental data. By integrating surrogate models, the approach is capable of addressing the high computational cost typically associated with FEM simulations. The methodology is validated through a series of experiments involving electric motor simulations and physical measurements, showcasing the effectiveness of Bayesian updates in refining FEM predictions. The results demonstrate that the proposed integrated approach significantly improves parameter estimation accuracy and motor performance prediction, ultimately contributing to more efficient design and operational optimization of electric motors.