Sliva, Marcelo D; Asef, Pedram; Badewa, Oluwaseun A; Alden, Rosemary E; Ionel, Dan; (2025) Torque-Speed Characteristic Estimation based on Gaussian Processes and Adaptive Sampling Strategy for Permanent Magnet Synchronous Machines. In: Proceedings of the IEEE Energy Conversion Congress and Exposition, ECCE. (pp. pp. 1-6). IEEE (In press).

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Abstract

—Internal Permanent Magnet Synchronous Machines (IPMs) are widely used and typically optimized to meet specific performance requirements. Parameters such as base speed, maximum torque, and maximum speed commonly define the torquespeed characteristic of a given design. This study introduces a novel machine learning approach for statistically estimating the torque-speed characteristics of IPMs using Gaussian Process Regression (GPR), which models predictions as random variables. By leveraging uncertainty quantification, the study explores sampling strategies that enable the construction of a high-precision meta-model with minimal error and uncertainty. The proposed adaptive sampling strategy, combined with GPR, accurately estimates torque-speed characteristics and associated losses across the design space for the first time. This new method uses only a limited number of Finite Element Method (FEM)- based simulations, showing high accuracy with 12 FEM-based simulations. The results demonstrate good agreement with full FEM evaluations and experimental measurements, validating the effectiveness of the proposed method.

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