Volume 19, Issue 3 (September 2023)                   IJEEE 2023, 19(3): 130-151 | Back to browse issues page


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Jabbari A, Badran A. Segmented Hybrid-PM Machines for Electric Vehicles; Semi-Analytical, Numerical and Experimental Study. IJEEE 2023; 19 (3) :130-151
URL: http://ijeee.iust.ac.ir/article-1-2792-en.html
Abstract:   (671 Views)
Cost reduction, increased efficiency and reliability, extended service life, reduced noise and vibration, and environmental friendliness are critical for new generation wind turbines and electric vehicles. Segmented Hybrid Permanent Magnet (SHPM) machines, on the other hand, which are primarily segmented PMs combined with different materials, dimensions, and magnetization directions, offer a way to meet these needs. In this study, we present nine topologies of segmented PM-rotor SHPM generators based on the Taguchi experimental design method, while presenting a simple and accurate model based on subdomain method for estimating the magnetic performance characteristics of SHPM machines. An analytical model is provided. Magnetic partial differential equations (MPDEs) are represented in a pseudo-Cartesian coordinate system, and with appropriate boundary conditions (BC) and interface conditions (IC), the general solution and its Fourier coefficients are extracted using a variable separation approach. The performance characteristics of nine of the SHPM machines studied were compared semi-analytically and numerically. Two prototype SHPM machines were manufactured and semi-analytical modeling results were compared with finite element analysis (FEA) methods and experimental testing (load mode) on a generator. The FEA simulation and experimental test results have a maximum error rate of about 3, confirming the high accuracy of the provided semi-analytical model. We compare the induced voltage, torque ripple and magnetic torque among the investigated topologies.
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Type of Study: Research Paper | Subject: Special Electric Machines
Received: 2023/02/01 | Revised: 2023/11/25 | Accepted: 2023/10/23

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.