In some applications such as electric vehicles, electric motors should operate in a wide torque and speed ranges. An efficiency map is the contour plot of the maximum efficiency of an electric machine in torque-speed plane. It is used to provide an overview on the performance of an electric machine when operates in different operating points. The electric machine losses in different torque and speed operating points play a prominent role in the efficiency of the machines. In this paper, an overview about the change of various loss components in torque-speed envelope of the electric machines is rendered to show the role and significance of each loss component in a wide range of torque and speeds. The research gaps and future research subjects based on the conducted review are reported. The role and possibility of the utilization of the computational intelligence-based modeling of the losses in improvement of the loss estimation is discussed.

Efficiency maps (EffMs) illustrate the maximum efficiencies of electric machines over a range of torque-speed operating points. This paper reviews the available techniques for efficiency map calculation and gives a general understanding of EffM modelling and interpretation for traction motors. It shows the accuracy versus calculation effort trade-off for different methods. The process of efficiency map calculation for sample induction motor and permanent magnet synchronous machines is explained. Calculation of EffMs using commercial software is also covered. Finally, possible future research opportunities in this field are suggested.

Consideration of the overload (OL) performance of electric machines designed for EVs enables increasing the power density of the propulsion system. This paper aims to show the characteristics and advantages of the optimal IMs which have the capability of handling OL. A subdomain model (SDM) with the capability of the saturation prediction is developed and validated using experimental data. A lumped thermal model is developed to predict the transient temperature variation of the IMs. The thermal model is validated using the Motor-CAD transient thermal analysis. The fast speed and accuracy of the applied SDM allows to select twelve variables in a large search space for the optimization purpose. Initially, an optimization procedure is proposed to design three IMs over three different driving cycles. The optimal designs are validated from the electromagnetic and thermal aspects by the finite element analysis. IMs are then designed optimally with consideration of the OL capability. A transient thermal analysis is carried out to validate the designs. The optimal designs with and without consideration of OL are compared in terms of the machine parameters and geometry to understand how dimensions and equivalent circuit parameters of the IMs vary according to the driving cycles. The comparison allows more intuition about the consideration of OL capability in design.

In this article, the analysis of induction machines using five separated subdomains including the rotor slot, rotor slot opening, airgap, stator slot opening, and stator slot regions is considered. The Maxwell equations of each region are solved considering the boundary conditions. Uniform distributions of the current density in the rectangular slots of both the rotor and stator are considered to find the energy of the winding and to calculate the machine inductances using the energy equation. The calculated flux density of each subdomain is used to find the flux distribution. The calculated magnetic flux intensities are required to estimate the saturation level of the motor cores and correct the saturation-related values such as the leakage and magnetizing inductances. The machine core losses are calculated based on the Steinmetz equation at the no-load condition. The calculated inductances and resistances are used in the equivalent circuit of the squirrel-cage induction machine (SCIM) for the performance prediction. The results demonstrate the reliability of the saturation model in the prediction of the saturation influence in a wide range of magnetization levels. The accuracy of the proposed model in the prediction of the performance of the SCIM is validated using two-dimensional (2-D) and 3-D finite-element analysis results. A sensitivity analysis with 75 different geometries was conducted to show the capability of the model for the performance prediction of a wide range of induction machines.