Optimization of Axial-Flux Induction Motors for the Application of Electric Vehicles Considering Driving Cycles

Babak DIanati, Solmaz Kahourzade, Amin Mahmoudi

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1522-1533
Number of pages12
Issue number3
Publication statusPublished - Sep 2020


  • Analytical model
  • axial-flux induction motor (AFIM)
  • driving cycle
  • electric vehicle
  • optimal design


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