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In this paper, we introduce a novel method for establishing an efficiency map of interior permanent-magnet synchronous motors that are used for electric vehicle propulsion, by employing the finite-element method (FEM) and a neural network (NN) to reduce the analysis time. The electro-magnetic analysis of motors using the FEM, particularly iron loss analysis, is significantly time-consuming owing to the nonlinearity and the post-processing. Moreover, to obtain an efficiency map, a data map of the d-q flux linkages based on the d-q currents should be established. At this stage, we compute the flux densities in all the elements, and they are learned by the NN to obtain a function of the d-q currents. Subsequently, the iron losses at all operating points are calculated using the learned data via the harmonic loss method. The results of the proposed method indicate that the time required to obtain the efficiency map is reduced; furthermore, the results are validated via a comparison with the FEM results.

A Novel Method for Establishing an Efficiency Map of IPMSMs for EV Propulsion Based on the Finite-Element Method and a Neural Network