Optimization of High Efficiency Permanent Magnet Synchronous Machine Using Multi-objective Differential Evolution

  • M. Rezal
  • Dahaman IshakEmail author
  • Tiang Tow Leong
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)


This paper presents an optimization of surface-mounted permanent magnet synchronous machine (PMSM) based on analytical sub-domain model (ASM) together with differential evolution (DE). A three-phase, 15-slot/10-pole, PMSM is selected in the design with initial motor parameters which have been determined from sizing equations. Five motor parameters are to be optimized i.e. magnet thickness, airgap length, slot-opening width, magnet arc, and stator inner radius. Three objective functions are chosen i.e. to have the lowest total harmonic distortions in the induced phase back-emf, highest output torque and highest efficiency. The optimization of 15-slot/10-pole PMSM is further analyzed by comparing with other optimization algorithms i.e. genetic algorithm (GA), and particle swarm optimization (PSO). From the results, it is observed that PSO has the fastest computing time compared to GA and DE. Whereas, DE is approximately 55% faster than GA. The design work for PMSM can potentially become computationally intelligent without compromising the accuracy. While repetitive changes in motor parameters in finite element modeling could be avoided after applying this Differential Evolution.


Multi-objective optimization Permanent magnet synchronous machine Differential evolution 



The authors would like to thank Universiti Sains Malaysia and Malaysian Ministry of Higher Education for the financial support under FRGS grant scheme with project number 203/PELECT/6071328.


  1. 1.
    Yilmaz, M., Krein, P.T.: Capabilities of finite element analysis and magnetic equivalent circuits for electrical machine analysis and design. In: PESC—IEEE Power Electronics Specialists Conference, pp. 4027–4033 (2008)Google Scholar
  2. 2.
    Sizov, G.Y., lonel, D.M., Demerdash, N.A.O.: A review of efficient FE modeling techniques with applications to PM AC machines. In: IEEE Power and Energy Society General Meeting, pp. 1–6 (2011)Google Scholar
  3. 3.
    Cvetkovski, G., Petkovska, L., Leffiey, P.: Optimal design of single phase permanent magnet brushless DC motor using particle swarm optimisation. COMPEL Int. J. Comput. Math. Electr. Electron. Eng. 33(6), 1863–1876 (2014)Google Scholar
  4. 4.
    Gilson, A., Tavernier, S., Dubas, F., Depernet, D., Espanet, C.: 2-D analytical subdomain model for high-speed permanent-magnet machines. In: 18th International Conference on Electrical Machines and Systems (ICEMS), Pattaya, pp. 1508–1514 (2015)Google Scholar
  5. 5.
    Stipetic, S., Miebach, W., Zarko, D.: Optimization in design of electric machines: methodology and workflow. In: ACEMP—OPTIM—ELECTROMOTION Joint Conference, 2015. Online: 24 March 2016
  6. 6.
    Shamekhi, A.: An improved differential evolution optimization algorithm. In: Int. J. Res. Rev. Appl. Sci. 15(2), 132–145 (2013)Google Scholar
  7. 7.
    Rezal, M., Ishak, D.: Rotating analysis of 18-slot/16-pole permanent magnet synchronous motor for light electric vehicle using FEM. In: PECon—IEEE International Conference on Power and Energy, pp. 946–949 (2012)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Malaysian Spanish InstituteUniversiti Kuala LumpurKulimMalaysia
  2. 2.School of Electrical and Electronic EngineeringUniversiti Sains MalaysiaNibong TebalMalaysia
  3. 3.School of Electrical System EngineeringUniversiti Malaysia PerlisArauMalaysia

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