Abstract
This study utilizes the machine learning (ML) technique to estimate the power loss of surface-mounted Permanent Magnet Synchronous Motor (PMSM) for More-Electric Aircraft (MEA). Existing approaches do not consider ML methods in power loss calculation and only depend on empirical correction factors. The proposed ML aided model is proved to be more precise. Matching the analytical loss estimation with finite-element analysis (FEA) is the main research goal which includes two aspects: iron loss and permanent magnet (PM) loss. They are both based on conventional formulae but this study analyzes the limitation of these equations and the ML correction model can provide dedicated factors for the analytical motor model to make sure that the loss estimation is accurate in the whole motor design space. Average correction factor (ACF) approach is regarded as the comparison method to verify the excellent performance of the proposed ML model.
Highlights
With the continual development of magnet material, permanent magnet (PM) machines have been widely applied in electrical vehicles, fans, drives, and compressors due to their high efficiencies [1,2,3,4,5,6]
This study proposes a new method for loss estimation of machine stator and PM realized by using machine learning (ML) method to bridge the gap between analytical and finite-element analysis (FEA) models
The basic thinking of Average correction factor (ACF) is firstly using the boundary values of design variables (DVs) to provide correction factors for analytical loss estimation of PM, tooth, back-iron, and validate these factors using more sample data in a motor optimization problem
Summary
With the continual development of magnet material, permanent magnet (PM) machines have been widely applied in electrical vehicles, fans, drives, and compressors due to their high efficiencies [1,2,3,4,5,6]. In [2], a classical estimation model considering both hysteresis and eddy-current losses was proposed; the correction factors obtained by theoretically derived formulas are substantial and yield inaccurate results due to missing the domain wall motion This classical model was improved by adding an excess eddy current loss term in [3] which can be useful but needs many iterations for the motor initial design. This study proposes a new method for loss estimation of machine stator and PM realized by using machine learning (ML) method to bridge the gap between analytical and FEA models It utilizes simple conventional equations and databased ML training no need for substantial derivations. Based on FEM/experimental data collection, this approach can be applied to the holistic loss estimation of the motor even the whole actuation system
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