Design and Thermal Modeling of Modular Hybrid Excited Double-Sided Linear Flux Switching Machine

Himayat Ullah Jan, Faisal Khan, Basharat Ullah, Muhammad Qasim, Malak Adnan Khan, Ghulam Hafeez and Fahad Raddah Albogamy
Additional contact information
Himayat Ullah Jan: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
Faisal Khan: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
Basharat Ullah: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
Muhammad Qasim: Department of Electrical and Computer Engineering, COMSATS University Islamabad, Abbottabad 22060, Pakistan
Malak Adnan Khan: Department of Electronics Engineering, University of Engineering and Technology Peshawar, Abbottabad 22060, Pakistan
Ghulam Hafeez: Open AI Lab, National Yunlin University of Science and Technology, Douliou 64002, Taiwan
Fahad Raddah Albogamy: Computer Sciences Program, Turabah University College, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

Energies, 2021, vol. 14, issue 24, 1-21

Abstract: This paper presents a Hybrid Excited Double-Sided Linear Flux Switching Machine (HEDSLFSM) with a crooked tooth modular stator. Generally, the conventional stators are made of a full-length iron core, increasing manufacturing costs and iron losses. Higher iron losses result in lower efficiency and lower overall performance. A U-shaped modular stator with a crooked tooth is used to lower iron consumption and increase the machine’s efficiency. Ferrite magnets are used to replace rare earth magnets, which also reduces the machine cost. Two DC excitation windings are used above and below the ferrite magnet to reduce the PM volume. 2D electromagnetic performance analysis is done to observe the key performance indices. Geometric optimization is used to optimize the Split Ratio (S.R), DC winding slot area (DCw), and AC winding slot area (ACw). Stator Tooth Width (STW), space between the modules (S.S.), and crooked angle ( α ) are optimized through JMAG in-built Genetic Algorithm (G.A.) optimization. High thrust force density and modular stator make it a good candidate for long-stroke applications like railway transits. The thermal analysis of the machine is performed by FEA analysis and then validated by 2D LPMC (Lumped Parametric Magnetic Equivalent Circuit) model. Both analyses are compared, and an error percentage of less than 4% is achieved.

Keywords: ferrite magnet; genetic optimization; LPMC model; modular stator; efficiency; FEA (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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