Electromagnetic performance analysis of hybrid-excited flux-switching machines for electrical vehicles by an improved magnetic network model

Author(s):  
Wei Hua ◽  
Gan Zhang ◽  
Ming Cheng
Author(s):  
Basharat Ullah ◽  
Faisal Khan ◽  
Bakhtiar Khan ◽  
Muhammad Yousuf

Purpose The purpose of this paper is to analyze electromagnetic performance and develop an analytical approach to find the suitable coil combination and no-load flux linkage of the proposed hybrid excited consequent pole flux switching machine (HECPFSM) while minimizing the drive storage and computational time which is the main problem in finite element analysis (FEA) tools. Design/methodology/approach First, a new HECPFSM based on conventional consequent pole flux switching permanent machine (FSPM) is proposed, and lumped parameter magnetic network model (LPMNM) is developed for the initial analysis like coil combination and no-load flux linkage. In LPMNM, all the parts of one-third machine are modeled which helps in reduction of drive storage, computational complexity and computational time without affecting the accuracy. Second, self and mutual inductance are calculated in the stator, and dq-axis inductance is calculated using park transformation in the rotor of the proposed machine. Furthermore, on-load performance analysis, like average torque, torque density and efficiency, is done by FEA. Findings The developed LPMNM is validated by FEA via JMAG v. 19.1. The results obtained show good agreement with an accuracy of 96.89%. Practical implications The proposed HECPFSM is developed for high-speed brushless AC applications like electric vehicle (EV)/hybrid electric vehicle (HEV). Originality/value The proposed HECPFSM offers better flux regulation capability with enhanced electromagnetic performance as compared to conventional consequent pole FSPM. Moreover, the developed LPMNM reduces drive storage and computational time by modeling one-third of the machine.


2016 ◽  
Vol 27 (5) ◽  
pp. 961-967 ◽  
Author(s):  
Peiyuan Lian ◽  
◽  
Congsi Wang ◽  
Wei Wang ◽  
Binbin Xiang ◽  
...  

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