Modeling and Parametric Design of Permanent-Magnet AC Machines Using Computationally Efficient Finite-Element Analysis

TLDR

Computationally efficient finite‑element analysis (CE‑FEA) exploits symmetries in sine‑wave current‑regulated synchronous machines, yielding substantial computational savings. The paper presents the CE‑FEA method and applies it to a parametric design study of an interior‑permanent‑magnet machine. CE‑FEA evaluates motor performance through Fourier analysis of a minimal set of magnetostatic solutions and examines how stator tooth width, pole arc, and slot opening influence torque, efficiency, and ripple to derive design trends. CE‑FEA accurately estimates steady‑state performance indices and cuts simulation time by roughly two orders of magnitude, enabling rapid evaluation of 100 candidate designs in under 20 minutes.

Abstract

Computationally efficient finite-element analysis (FEA) (CE-FEA) fully exploits the symmetries of electric and magnetic circuits of sine-wave current-regulated synchronous machines and yields substantial savings of computational efforts. Motor performance is evaluated through Fourier analysis and a minimum number of magnetostatic solutions. The major steady-state performance indices (average torque, ripple and cogging torque, back-electromotive-force waveforms, and core losses) are satisfactorily estimated as compared with the results of detailed time-stepping (transient) FEA. In this paper, the CE-FEA method is presented and applied to a parametric design study for an interior-permanent-magnet machine. Significant reduction of simulation times is achieved (approximately two orders of magnitude), permitting a comprehensive search of large design spaces for optimization purposes. In this case study, the influence of three design variables, namely, stator tooth width, pole arc, and slot opening, on three performance indices, namely, average torque, efficiency, and full-load torque ripple, is examined, and design trends are derived. One hundred candidate designs are simulated in less than 20 minutes on a state-of-the-art workstation.