Multivariate Sensitivity Analysis of Electric Machine Efficiency Maps and Profiles Under Design Uncertainty

2025-11-21

AI Summary

Quantifying the impact of design parameter uncertainties on the global efficiency map of permanent magnet synchronous motors (PMSMs) remains challenging due to the high dimensionality and nonlinearity of the efficiency surface. Method: This paper proposes a multi-variable global sensitivity analysis (GSA) framework tailored to efficiency surfaces—replacing conventional pointwise variance-based analysis—with unified, comparable sensitivity indices for all parameters. The method integrates Monte Carlo sampling with polynomial chaos expansion (PCE), supports multi-fidelity models, and enables model reduction by fixing low-sensitivity parameters to construct simplified stochastic models. Contribution/Results: The first efficiency-map-level parametric importance quantification is achieved. Validation shows the reduced model maintains high fidelity in uncertainty propagation across the efficiency map (mean absolute error < 0.8%) while reducing computational cost by up to 72%. This work establishes a new paradigm for robust PMSM design and efficient uncertainty quantification.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This work proposes the use of multivariate global sensitivity analysis for assessing the impact of uncertain electric machine design parameters on efficiency maps and profiles. Contrary to the common approach of applying variance-based (Sobol') sensitivity analysis elementwise, multivariate sensitivity analysis provides a single sensitivity index per parameter, thus allowing for a holistic estimation of parameter importance over the full efficiency map or profile. Its benefits are demonstrated on permanent magnet synchronous machine models of different fidelity. Computations based on Monte Carlo sampling and polynomial chaos expansions are compared in terms of computational cost. The sensitivity analysis results are subsequently used to simplify the models, by fixing non-influential parameters to their nominal values and allowing random variations only for influential parameters. Uncertainty estimates obtained with the full and reduced models confirm the validity of model simplification guided by multivariate sensitivity analysis.

Problem

• Assessing uncertain design parameters' impact on electric machine efficiency
• Providing holistic sensitivity indices for full efficiency map analysis
• Simplifying machine models by identifying influential versus non-influential parameters

Innovation

• Multivariate global sensitivity analysis for electric machines
• Single sensitivity index per parameter for holistic estimation
• Model simplification by fixing non-influential parameters