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This paper presents an improved magnetic equivalent circuit (MEC) method for modeling linear permanent-magnet synchronous motors (LPMSMs) with adjustable accuracy. The performance of machine with different dimensions, poles and slot numbers can be studied by the proposed flexible MEC, where the core nonlinearity is fitted on the material B–H curve. End effect is modeled by considering two virtual zones with desired accuracy at both entrance and exit ends of the primary. A new structure based on magnets segmentation is also proposed to investigate its effect on the motor performance. Finally, the results of the proposed method are compared with 3D-FEM to show the effectiveness of the presented model. The results show improvement in processing time with good accuracy compared to previous classic methods. In general, introducing a new model based on an improved MEC approach for modeling LPMSMs considering slot effect, iron core saturation and segmented PMs with flexible accuracy by adjusting the number of flux tubes is the paper novelty which is studied in this work. [ABSTRACT FROM AUTHOR]

This article presents a novel analysis on a new DC-Excited Flux-Switching Machine (DC-FSM) for Ground Power Unit (GPU) application. The performance of machine under different structures can be studied by a flexible Magnetic Equivalent Circuit (MEC), where the core non-linearity can be fixed on the material B-H curve. The model accuracy can also be adjusted arbitrary by determining the number of proposed MEC elements (flux tubes) for a given structure. The performance analysis of two sample DC-FSMs as two high-frequency 120 V generators is performed by the proposed MEC and validated by 2D and 3D FEM based simulations. The obtained results show a very good improvement in processing time with valuable accuracy. In general, introducing a new DC-FSM for the GPU application and analyzing its performance by proposing a modified MEC approach are the paper novelties which are studied in this work. Since the suggested MEC is more flexible than FEM, it is recommended for designing, modeling, and optimization purposes. [ABSTRACT FROM AUTHOR]