Optimization and finite element analysis of an in-wheel permanent magnet motor
DOI:
https://doi.org/10.11113/mjfas.v17n1.1981Keywords:
Multi-objective optimization, PSO-based techniques, permanent magnet machine, finite element analysisAbstract
Nowadays, artificial intelligence techniques have become widely used in electrical machines optimization and design. In this attempt, different methods have been proposed. The present work is devoted to the electromagnetic analysis and design of an in-wheel radial flux outer rotor surface mounted permanent magnet motor (SMPM) for electric vehicle application. Two based-Swarm Particle Optimization (PSO) techniques, namely, the improved PSO multi-Objective optimization and the Speed-constrained Multi-objective PSO Optimization, are applied. The main idea of the current optimization procedure is to determine the optimal machine sizing parameters providing the maximum of efficiency with the minimum of weight. To reach this goal, two objective functions are employed the efficiency maximization and the weight minimization. A preliminary analytical model describing the magnetic and electrical machine features was presented. In order to find the optimum design, the optimization results are discussed and analyzed. In addition, the electromagnetic performances of the found optimum design were studied by means of finite element analysis (FEA) and compared with those obtained by the optimization procedure.
References
Zhu ZQ, Howe D. Electrical machines and drives for electric, hybrid and fuel cell vehicles. P IEEE 2007; 95: 746–765.
Wang J, Atallah K, Zhu ZQ, Howe D. Modular 3-phase permanent magnet brushless machines for in-wheel applications. IEEE T Veh Technol 2008; 57: 2714–720.
Strous TD. Design of a permanent magnet radial flux concentrated coil generator for a range extender application. MSc, Delft University of Technology, Netherlands, 2010.
Chan CC, Chau KT. Modern electric vehicle technology. New York, USA: Oxford University Press, 2001.
Wrobel R, Mellor P. Design considerations of a direct drive brushless machine with concentrated windings. IEEE T Energy Conver 2008; 23: 1-8.
Rix A, Kamper M, Wang R. Design and performance evaluation of concentrated coil permanent magnet machines for in-wheel drives. In: IEEE 2007 International Electric Machines and Drives Conference; 3-5 May 2007; Antalya, Turkey.
Andriollo M, Bettanini G, Martinelli G, Morini A, Stellin S, Tortella A. In-wheel permanent magnet motors for public transport application. In: 5th 2005 International Conference on Electric Power Systems, High Voltages, Electric Machines; 16-18 December 2005; Tenerife, Canary Islands, Spain.
Wu D, Fei W, Luk PCK, Xia B. Design Considerations of Outer-Rotor Permanent Magnet Synchronous Machines for In-Wheel Electric Vehicle Applications using Particle Swarm Optimization. In: IET 2014 7th International Conference on Power Electronics, Machines and Drives; 8-10 April 2014; Manchester, United Kingdom.
Hung VX. Modelling of exterior rotor permanent magnet machines with concentrated windings, MSc, Faculty of electrical engineering, mathematics and computer science, Netherlands, 2012.
Markovic M, Ragot P, Perriard Y. Design optimization of a BLDC motor: a comparative analysis. In: IEEE 2007 International Electric Machines and Drives Conference; 3-5 May 2007; Antalya, Turkey.
Ilka R, Tilaki A R, Alamdari HA, Baghipour R. Design Optimization of Permanent Magnet-Brushless DC Motor using Elitist Genetic Algorithm with Minimum loss and Maximum Power Density. Int J Mecha, Elecr Comput Technol 2014; 4: 1169-1185.
Ahn Y, Park J, CG Lee, Kim JW, and Jung SY. Novel Memetic Algorithm implemented With GA (Genetic Algorithm) and MADS (Mesh Adaptive Direct Search) for Optimal Design of Electromagnetic System. IEEE T Magn 2010; 46: 1982-1996.
Sun X, Cai Y. Driving-Cycle Oriented Design Optimization of a Permanent Magnet Hub Motor Drive System for a Four-Wheel-Drive Electric Vehicle. IEEE Transactions on Transportation Electrification, 2020, 6: 1115-1125.
Izanlo A, Gholamian S. A, Abdollahi S. E. Optimal Design of a Permanent Magnet Synchronous Motor for High Efficiency and Low Cogging Torque. International Journal on Electrical Engineering and Informatics, 2020, 12: 173-186.
Sakthivel VP, Bhuvaneswari R, Subramanian S. Multi-objective parameter estimation of induction motor using particle swarm optimization. Eng Appl Artif Intel 2010; 23: 302–312.
Ashabani M, Rady YA, Mohamed I. Multi-objective Shape Optimization of Segmented Pole Permanent-Magnet Synchronous Machines with Improved Torque Characteristics. IEEE T Magn 2011; 47: 795-804.
Mutluer M, and Bilgin O. Design optimization of PMSM by particle swarm optimization and genetic algorithm. In: IEEE 2012 International Symposium on Innovations in Intelligent Systems and Applications; 2-4 July 2012; Trabzon, Turkey.
Sierra M R, Coello C. Improving PSO-based Multi-Objective Optimization using Crowding, Mutation and e –Dominance. In: 2005 Third International Conference on Evolutionary Multi-Criterion Optimization; 9-11 March 2005; Guanajuato, Mexico
Nebro AJ, Durillo JJ, Nieto JG, CAC, Luna F, Alba E. SMPSO: A New PSO-based Metaheuristic for Multi-objective Optimization. In: IEEE 2009 Symposium on Computational Intelligence in Multi-criteria Decision-Making; 30 March-02 April 2009; Nashville, TN, USA.
Mansouri A, Msaddek H, Trabelsi H. Optimum Design of a Surface Mounted Fractional Slots Permanent Magnet Motor. Int Rev Electr Eng 2015; 10: 28-35.
Feng Y. L, Zhang C. N. Analytical calculation for predicting the air gap flux density in surface-mounted permanent magnet synchronous machine. Journal of Electrical Engineering and Technology, 2017; 12: 769-777.
Bianchi N, Bolognani S. Design criteria of high efficiency SPM synchronous motors. IEEE T Energy Conver 2006; 21: 396-404.
Mansouri A, Smairi N, Trabelsi H. Multi-objective optimization of an in-wheel electric vehicle motor. International Journal of Applied Electromagnetics and Mechanics, 2016; 50: 449–465.
