Analytical design of axial-flux induction motors - Dianati, Babak; Kahourzade, Solmaz; Mahmoudi, Amin (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings, 2019)
In this paper, two-dimensional (2D) analytical model is proposed for axial-flux induction motor (AFIMs) based on the solution of magnetic vector potential equations, which also considers the saturationâ™s effect on the performance of the machine. The model is developed from the quasi- magneto-static Maxwellâ™s equations, which are analytically solved. It considers a uniform distribution of current density in the rectangular shaped rotor/stator slots. By computing the magnetic energy of the machine, this method obtains the inductances and uses them to assess the design performance parameters. The accuracy of the 2-D analytical solutions is verified against those obtained from the finite element method (FEM). The suggested model is generic and applicable to AFIMs with any number of poles and phases. A case study on optimization axial-flux induction motor is presented to evaluate the performance of the proposed model. The proposed method can replace the FEM in designing and optimization of the AFIMs.
CRIS-Team Scopus Importer:2020-02-21
Keywords: Analytical simulation; Axial-flux induction motor; Magnetic vector potential solution; Optimal design

Axial-flux induction motors for electric vehicles - Dianati, Babak; Kahourzade, Solmaz; Mahmoudi, Amin (2019 IEEE Vehicle Power and Propulsion Conference, VPPC 2019 - Proceedings, 2019)
This paper presents design optimization of axial- flux induction motors (AFIMs) for electric vehicles. The approach is based on an analytical resolution of the Maxwellâ™s equations inside a set of subdomains. The analytical method is verified against 2D finite-element analysis. It allows detailed modelling and accurate steady-state performance prediction of AFIMs in a very short time (under 1sec), making possible evaluation of numerous design for the optimization. The optimization effortlessly simulates and predict the performance of various AFIM designs based on driving cycles and vehicle limitations. An optimization based on driving cycle of electric vehicles is studied through considering a driving cycle and designing minimum-mass AFIMs capable of fulfilling it. It is observed that driving-cycle based motor designs benefit from mass reduction and ensures the feasibility of all operating conditions.
CRIS-Team Scopus Importer:2020-02-21
Keywords: Analytical modelling; Axial-flux induction motor; Driving cycle; Electric vehicle; Optimization

Nonlinear Modeling of MGSPMs Based on Hybrid Subdomain and Magnetic Equivalent Circuitry - Dianati, Babak; Hahn, Ingo (IEEE Transactions on Magnetics, 2022)
The need for combining two mechanical powers with electrical power is a vital part of many applications, such as hybrid electric vehicles. The magnetic geared surface permanent magnet (MGSPM) motor is a compact and efficient way of achieving this goal. However, their complex structure demands time-consuming optimizations, which requires a large number of simulations. In this context, a fast and accurate simulation method is required to reduce the design and optimization time. In addition to speed and accuracy in considering the complex structures of the machine, as the saturation effect has a major impact on the accuracy of the simulations in these machines, it must be included in the method. To tackle this problem, in this article, a hybrid subdomain (SD) and magnetic equivalent circuit (MEC) model is proposed, considering the saturation effect of the iron parts using current distributions in the SD model on the surfaces of modulators and rotor core, as well as in the stator slots. These currents are calculated using the MEC model of the respective parts and Ampere's law. Furthermore, an example motor is simulated both using the proposed method and the FE method, and the accuracy of the method both for prediction of magnetic flux density inside iron parts and prediction of flux linkages of the machine for a variety of designs and excitations is verified.
CRIS-Team WoS Importer:2021-12-31

Optimization of Axial-Flux Induction Motors for the Application of Electric Vehicles Considering Driving Cycles - Dianati, Babak; Kahourzade, Solmaz; Mahmoudi, Amin (IEEE Transactions on Energy Conversion, 2020)
This article presents the optimization of axial-flux induction motors (AFIMs) for the electric vehicles application by solving the Maxwell's equations inside five subdomains. It allows a detailed modelling and accurate steady-state performance prediction of the AFIMs in a short time (under 1 sec). The analytical model is verified against 2D finite-element analysis. Different design options of the AFIM are considered and then optimized based on the proposed analytical model with the aim of minimizing the motor's volume and satisfy a set of predefined performance parameters for operation in a nominal point and also over a driving cycle. For operation in a nominal point, three power levels and for the driving cycle based operation, three driving cycles are considered. In each operation state, maximum torque per ampere (MTPA) constraint is enforced by changing the number of winding turns per slots. Alternatively, similar optimizations are performed without MTPA constraint by considering the number of winding turns as a variable. It is observed that motor design based on the driving cycle benefits from a lower volume while it is feasible for all the operating conditions. Enforcing the MTPA in design leads to a wider torque-speed operating range and simpler control in the expense of additional volume.
CRIS-Team Scopus Importer:2020-09-11
Keywords: Analytical model; axial-flux induction motor (AFIM); driving cycle; electric vehicle; optimal design