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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.

In modern electric vehicles, electrical failure has become a critical problem that reduces the lifetime of traction motors. Moreover, traction motors with high-voltage and high-speed systems for a high power density have been aggravating the shaft voltage problems. This study identifies that direct-oil-cooling systems exacerbate this problem. To address this, an analytical method for calculating parasitic capacitance is proposed to determine the effects of cooling oil in a traction motor with a direct-oil-cooling system. Capacitance equivalent circuits are configured based on whether the slot is submerged in the cooling oil. In addition, an electric field decomposition method is applied to analyze the distortion of the electric field by the structure of the conduction parts in the motor. The results indicate that the parasitic capacitances of the traction motor are increased by the influence of the cooling oil resulting in an increase in the shaft voltage.

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In this paper, parameter optimization of multi-layered interior permanent-magnet synchronous motors for electric vehicle propulsion is carried out to improve torque ripple and efficiency at low-and high-speed regions. First, we establish a torque ripple map based on d-q currents. The ripples of the d-q parameters and harmonics of the phase flux linkages are then analyzed to investigate the relationship between the torque ripple and the parameters. Second, we compose and analyze an iron loss map based on d-q currents, and then investigate the parameters that affect the harmonics of the flux density. In particular, because the high-speed region is highly vulnerable to iron loss, the parameters related to this tendency are also analyzed. Based on the analysis results, we develop a parameter optimization strategy that leads to an optimal design. To analyze the electro-magnetic performances of the motors, two-and three-dimensional finite elements analyses are carried out by employing sinusoidal and inverter-controlled currents, and the improvement achieved are demonstrated by the experimental validations on prototypes.INDEX TERMS Efficiency, iron loss, low-and high-speed regions, multi-layered interior permanentmagnet synchronous motors, parameter optimization, torque ripple.