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Design Optimization Methods for Electrical Machines: A Review

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Abstract

Most of the appliances in industrial equipment and systems uses electric machines. They fill the various requirements for global sustainability not only physically or technologically but also environmentally. Therefore, progressively complex engineering domains and constraints are involved in the design optimization process such as electromagnetics, structural mechanics, and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models and algorithms. Several efficiency optimization methods are highlighted such as Gradient Based Algorithm, Tabu Search, Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Multi-objective Algorithm and Deterministic Optimization Method. Meanwhile, Deterministic Optimization Method has been presented on Field excitation, Permanent magnet and Hybrid excitation flux switching machines for the optimization. From the literature reviews, it is observed that DOM algorithms gained the best design technique for electric machines to produce optimal performances.

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References

  1. Liu X, Slemon GR (1991) An improved method of optimization for electrical machines. IEEE Trans Energy Convers 6(3):492–496

    Google Scholar 

  2. Cavagnino A, Bramerdorfer G, Tapia JA (2017) Optimization of electric machine designs—part I. IEEE Trans Ind Electron 64(12):9716–9720

    Google Scholar 

  3. Lei G, Zhu J, Guo Y, Liu C, Ma B (2017) A review of design optimization methods for electrical machines. Energies 10(12):1–31

    Google Scholar 

  4. Cavagnino A, Bramerdorfer G, Tapia JA (2018) Optimization of electric machine designs—part II. IEEE Trans Ind Electron 65(2):1700–1703

    Google Scholar 

  5. Dubas F, Sari A, Hissel D, Espanet C (2008) A comparison between CG and PSO algorithms for the design of a PM motor for fuel cell ancillaries. IEEE Vehicle Power and Propulsion Conference 2008:1–7

    Google Scholar 

  6. Liu K-y, Sheng W, Cheng S (2013) A novel improved sequential quadratic programming algorithm to solve DG dispatch in distribution system. In: International conference on electrical machines and systems (ICEMS), pp 1415–1420

  7. Kakaee A, Keshavarz M (2012) Comparison the sensitivity analysis and conjugate gradient algorithms for optimization of opening and closing angles of valves to reduce fuel consumption in XU7/L3 engine. Int J Automot Eng 2(3):143–155

    Google Scholar 

  8. Razik H, Defranoux C, Rezzoug A (2000) Identification of induction motor using a genetic algorithm and a quasi-Newton algorithm. In: 7th IEEE international power electronics congress. Technical proceedings (CIEP), pp 65–70

  9. Popa DC, Micu DD, Miron OR, Szabo L (2013) Optimized design of a novel modular tubular transverse flux reluctance machine. IEEE Trans Magn 49(11):5533–5542

    Google Scholar 

  10. de Paula Machado Bazzo T, Kolzer JF, Carlson R, Wurtz F, Gerbaud L (2017) Multiphysics design optimization of a permanent magnet synchronous generator. IEEE Trans Ind Electron 64(12):9815–9823

    Google Scholar 

  11. Chen Y, Ding Y, Zhuang J, Zhu X (2018) Multi-objective optimization design and multi-physics analysis a double-stator permanent-magnet doubly salient machine. Energies 11(8):1–15

    Google Scholar 

  12. Lanrong C, Dejin HU, Van JIA (2006) Optimal design of main girder of large pressing machine based on father-offspring combined selection GA. In: International technology and innovation conference (ITIC), pp 6–12

  13. Ho SL, Yang S, Ni G, Wong HC (2001) An improved Tabu search for the global optimizations of electromagnetic devices. IEEE Trans Magn 37(5):3570–3574

    Google Scholar 

  14. Bonthu SSR, Arafat A, Choi S (2017) Comparisons of rare-earth and rareearth-free external rotor permanent magnet assisted synchronous reluctance motors. IEEE Trans Ind Electron 64(12):9729–9738

    Google Scholar 

  15. Idoumghar L, Raminosoa T, Miraoui A (2009) New tabu search algorithm to design an electric motor. IEEE Trans Magn 45(3):1498–1501

    Google Scholar 

  16. Yang L, Ho SL, Fu WN (2014) Design optimizations of electromagnetic devices using sensitivity analysis and tabu algorithm. IEEE Trans Magn 50(11):1–4

    Google Scholar 

  17. Chen Y, Fu W, Weng X (2017) A concept of general flux-modulated electric machines based on a unified theory and its application to developing a novel doubly-fed dual-stator motor. IEEE Trans Ind Electron 64(12):9914–9923

    Google Scholar 

  18. Cho DH, Kim JK, Jung HK, Lee CG (2003) Optimal design of permanentmagnet motor using autotuning niching genetic algorithm. IEEE Trans Magn 39(3):1265–1268

    Google Scholar 

  19. Shen Y, Lu Q, Li H, Cai J, Huang X, Fang Y (2018) Analysis of a novel double-sided yokeless multitooth linear switched-flux PM motor. IEEE Trans Ind Electron 65(2):1837–1845

    Google Scholar 

  20. Nakata T, Sanada M, Morimoto S, Inoue Y (2017) Automatic design of IPMSMs using a genetic algorithm combined with the coarse-mesh FEM for enlarging the high-efficiency operation area. IEEE Trans Ind Electron 64(2):9721–9728

    Google Scholar 

  21. Nakata T, Sanada M, Morimoto S, Inoue Y, Fem AC (2016) Automatic design of IPMSMs using a GA coupled with the coarse-mesh finite element method. In: 19th International conference on electrical machines and systems (ICEMS), pp 1–6

  22. Verma SP (2011) Design optimization of 7.5 Kw, 4pole, 3-Phase, 50 Hz induction motor employing genetic algorithm/improved genetic algorithm using sweep frequency response analysis. MIT Int J Electr Instrum Eng 1(2):108–115

    Google Scholar 

  23. Gyorgy T, Biro KA (2015) Genetic Algorithm based design optimization of a three-phase induction machine with external rotor. In: International Aegean conference on electrical machines and power electronics (ACEMP), international conference on optimization of electrical and electronic equipment (OPTIM) and international symposium on advanced electromechanical motion systems (ELECTROMOTION), pp 462–467

  24. Peter I, Scutaru G, Nistor CG (2014) Manufacturing of asynchronous motors with squirrel cage rotor, included in the premium efficiency category IE3, at S.C. Electroprecizia Electrical-Motors S.R.L. In: international conference on optimization of electrical and electronic equipment (OPTIM), pp 421–425

  25. Li W, Wang P, Li D, Zhang X, Cao J, Li J (2017) Multiphysical field collaborative optimization of premium induction motor based on GA. IEEE Trans Ind Electron 65(2):1704–1710

    Google Scholar 

  26. Duan Y, Harley RG, Habetler TG (2009) Comparison of particle swarm optimization and genetic algorithm in the design of permanent magnet motors. In: IEEE 6th international power electronics and motion control conference, pp 822–825

  27. Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    MathSciNet  MATH  Google Scholar 

  28. Fan Q, Yan X (2016) Self-adaptive differential evolution algorithm with zoning evolution of control parameters and adaptive mutation strategies. IEEE Trans Cybern 46(1):219–232

    Google Scholar 

  29. Pei T, Li D, Qu R, Shah MR, Zhang P (2017) Multi-objective optimization algorithm of a magnetic field modulation motor based on advanced differential evolution. In: 20th International conference on electrical machines and systems (ICEMS), pp 1–5.157

  30. Chen XH, Guo XX, Pei JM, Man WY (2017) A hybrid algorithm of differential evolution and machine learning for electromagnetic structure optimization. In: 32nd youth academic annual conference of chinese association of automation (YAC), pp 755–759

  31. Gerada D, Mebarki A, Brown NL, Gerada C, Cavagnino A, Boglietti A (2014) High-speed electrical machines: technologies, trends, and developments. IEEE Trans Ind Electron 61(6):2946–2959

    Google Scholar 

  32. Fodorean D (2014) Study of a high-speed motorization with improved performances dedicated for an electric vehicle. IEEE Trans Magn 50(2):921–924

    Google Scholar 

  33. Uzhegov N, Kurvinen E, Nerg J, Pyrhonen J, Sopanen JT, Shirinskii S (2016) Multidisciplinary design process of a 6-slot 2-pole high-speed permanentmagnet synchronous machine. IEEE Trans Ind Electron 63(2):784–795

    Google Scholar 

  34. Huang Z, Fang J, Liu X, Han B (2016) Loss calculation and thermal analysis of rotors supported by active magnetic bearings for high-speed permanentmagnet electrical machines. IEEE Trans Ind Electron 63(4):2027–2035

    Google Scholar 

  35. Fodorean D, Idoumghar L, Brevilliers M, Minciunescu P, Irimia C (2017) Hybrid differential evolution algorithm employed for the optimum design of a high-speed PMSM used for EV propulsion. IEEE Trans Ind Electron 64(12):9824–9833

    Google Scholar 

  36. Kennedy J, Eberhart R (1989) Particle swarm optimization. Proc Int Conf Neural Netw (ICNN) 4(1–3):1942–1948

    Google Scholar 

  37. Yu R, Xiaobai X (2008) Optimization research of PSO-PID algorithm for the design of brushless permanent magnet machines. In: IEEE international symposium on embedded computing (SEC), pp 26–30

  38. Zhang C, Sun R, Liu C, Fan Y, Niu S, Song Y (2006) An improved particle swarm optimization and its application to power system transfer capability calculation. In: International conference on power system technology, pp 1–5

  39. Zielinski K, Laur R (2007) Stopping criteria for a constrained single-objective particle swarm optimization algorithm. J Inform 31(1):51–59

    MATH  Google Scholar 

  40. Dos Santos CL, Ayala HVH, Alotto P (2010) A multiobjective gaussian particle swarm approach applied to electromagnetic optimization. IEEE Trans Magn 46(8):3289–3292

    Google Scholar 

  41. Lee JH, Kim JW, Song JY, Kim YJ, Jung SY (2016) A novel memetic algorithm using modified particle swarm optimization and mesh adaptive direct search for PMSM design. IEEE Trans Magn 52(3):1–4

    Google Scholar 

  42. Lee JH, Song JY, Kim DW, Kim JW, Kim YJ, Jung SY (2017) Particle swarm optimization algorithm with intelligent particle number control for optimal design of electric machines. IEEE Trans Ind Electron 65(2):1791–1798

    Google Scholar 

  43. Fei W, Luk PCK (2010) A new technique of cogging torque suppression in direct-drive permanent-magnet brushless machines. IEEE Trans Ind Appl 46(4):1332–1340

    Google Scholar 

  44. Zhu L, Jiang SZ, Zhu ZQ, Chan CC (2009) Analytical methods for minimizing cogging torque in permanent-magnet machines. IEEE Trans Magn 45(4):2023–2031

    Google Scholar 

  45. Zhu L, Jiang SZ, Zhu ZQ, Chan CC (2008) Comparison of alternate analytical models for predicting cogging torque in surface-mounted permanent magnet machines. In: IEEE vehicle power and propulsion conference, pp 1–6

  46. Zarko D, Ban D, Lipo TA (2009) Analytical solution for electromagnetic torque in surface permanent-Magnet motors using conformal mapping. IEEE Trans Magn 45(7):2943–2954

    Google Scholar 

  47. Wang D, Wang X, Qiao D, Pei Y, Jung SY (2011) Reducing cogging torque in surface-mounted permanent-magnet motors by nonuniformly distributed teeth method. IEEE Trans Magn 47(9):2231–2239

    Google Scholar 

  48. Dosiek L, Pillay P (2007) Cogging torque reduction in permanent magnet machines. IEEE Trans Ind Appl 43(6):1565–1571

    Google Scholar 

  49. Wu LJ, Zhu ZQ, Staton DA, Popescu M, Hawkins D (2012) Comparison of analytical models of cogging torque in surface-mounted PM machines. IEEE Trans Ind Electron 59(6):2414–2425

    Google Scholar 

  50. Pristup AG, Toporkov DM, Shevchenko AF (2014) A study of cogging torque in permanent magnet synchronous machines with fractional slot windings. J Russ Electr Eng 85(12):743–747

    Google Scholar 

  51. Zhu ZQ, Howe D (2000) Influence of design parameters on cogging torque in permanent magnet machines. IEEE Trans Energy Convers 15(4):407–412

    Google Scholar 

  52. Xue Z, Li H, Zhou Y, Ren N, Wen W (2017) Analytical prediction and optimization of cogging torque in surface-mounted permanent magnet machines with modified particle swarm optimization. IEEE Trans Ind Electron 64(12):9795–9805

    Google Scholar 

  53. Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325

    MathSciNet  MATH  Google Scholar 

  54. Parsopoulos KE, Vrahatis MN (2005) Unified particle swarm optimization for solving constrained engineering optimization problems. In: Advances in natural computation, pp 582–591

  55. Qu J, Huang Y, Guo B, Yang H, Fang S (2017) An optimal design of an AFPMSM using analytical approach and particle swarm optimization. In: 2017 20th international conference on electrical machines and systems (ICEMS), pp 1–5

  56. Lee JH, Kim JW, Song JY, Kim DW, Kim YJ, Jung SY (2017) Distance-based intelligent particle swarm optimization for optimal design of permanent magnet synchronous machine. IEEE Trans Magn 53(6):1–4

    Google Scholar 

  57. Lei G, Zhu J, Guo Y, Liu C, Ma B (2017) A review of design optimizationmethods for electrical machines. Energies 10(12):1–31

    Google Scholar 

  58. Jolly L, Jabbar MA, Qinghua L (2005) Design optimization of permanentmagnet motors using response surface methodology and genetic algorithms. IEEE Trans Magn 41(10):3928–3930

    Google Scholar 

  59. Ma C, Qu L (2015) Multiobjective optimization of switched reluctance motorsbased on design of experiments and particle swarm optimization. IEEE Trans Energy Convers 30(3):1144–1153

    Google Scholar 

  60. Duan Y, Ionel DM (2013) A review of recent developments in electricalmachine design optimization methods with a permanent-magnet synchronousmotor benchmark study. IEEE Trans Ind Appl 49(3):1268–1275

    Google Scholar 

  61. Zhu X, Shu Z, Quan L, Xiang Z, Pan X (2016) Multi-objective optimization of an outer-rotor V-shaped permanent magnet flux switching motor basedon multi-level design method. IEEE Trans Magn 52(10):1–8

    Google Scholar 

  62. Kackar RN (1985) Off-line quality control, parameter design, and the taguchimethod. J Qual Technol 17(4):176–188

    Google Scholar 

  63. Hwang CC, Chang CM, Liu CT (2013) A fuzzy-based taguchi method formultiobjective design of PM motors. IEEE Trans Magn 49(5):2153–2156

    Google Scholar 

  64. Lin CH, Hwang CC (2016) Multiobjective optimization design for a sixphase copper rotor induction motor mounted with a scroll compressor. IEEE Trans Magn 52(7):1–4

    Google Scholar 

  65. Lin C-H (2015) Application of hybrid recurrent Laguerre-orthogonal-polynomialNN control in V-belt continuously variable transmission system using modifiedparticle swarm optimization. J Mech Sci Technol 29(9):3933–3952

    Google Scholar 

  66. Hwang CC, Chang CM, Liu CT (2013) Design considerations for spindleSPM motors with minimized usage of rare-earth magnets. IEEE Trans Magn 49(7):3925–3928

    Google Scholar 

  67. Yamazaki K, Suzuki A, Ohto M, Takakura T, Nakagawa S (2011) Equivalentcircuit modeling of induction motors considering stray load loss and harmonictorques using finite element method. IEEE Trans Magn 47(5):986–989

    Google Scholar 

  68. Ahn J, Lee D, Park GJ, Kim YJ, Kim J, Jung SY (2014) Numerical designcompatibility of induction motor with respect to voltage and current sources. IEEE Trans Magn 50(2):773–776

    Google Scholar 

  69. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitistmultiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Google Scholar 

  70. Krishnapriya PFKRS (2016) A survey on non-dominated sortinggenetic algorithm ii and its applications. Int J Res Comput Appl Robot 4(6):7–11

    Google Scholar 

  71. Sindhya K, Manninen A, Miettinen K, Pippuri J (2017) Design of a permanentmagnet synchronous generator using interactive multiobjectiveoptimization. IEEE Trans Ind Electron 64(12):9776–9783

    Google Scholar 

  72. Miettinen K, Mäkelä MM (2006) Synchronous approach in interactivemultiobjective optimization. Eur J Oper Res 170(3):909–922

    MATH  Google Scholar 

  73. Pereira LA, Haffner S, Nicol G, Dias TF (2017) Multiobjective optimizationof five-phase induction machines based on NSGA-II. IEEE Trans Ind Electron 64(12):9844–9853

    Google Scholar 

  74. Krasopoulos CT, Armouti IP, Kladas AG (2017) Hybrid multi-objectiveoptimization algorithm for PM motor design. IEEE Trans Magn 53(6):1–4

    Google Scholar 

  75. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numericalfunction optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    MATH  Google Scholar 

  76. Beniakar ME, Kakosimos PE, Kladas AG (2015) Strength paretoevolutionary optimization of an in-wheel PM motor with unequal teeth forelectric traction. IEEE Trans Magn 51(3):1–4

    Google Scholar 

  77. Baatar N, Zhang D, Koh CS (2013) An improved differential evolutionalgorithm adopting λ-best mutation strategy for global optimization ofelectromagnetic devices. IEEE Trans Magn 49(5):2097–2100

    Google Scholar 

  78. Sulaiman E, Kosaka T (2012) Parameter sensitivity study for optimization offield-excitation flux switching synchronous machine for hybrid electricvehicles. In: 2012 7th IEEE conference on industrial electronics andapplications (ICIEA), pp 52–57

  79. Sulaiman E, Omar MF, Hakami SS (2016) Optimization of 6Slots-7Poles &12Slots-14Poles flux-switching permanent magnet machines for plug-in HEV. In: International conference on control, electronics, renewable energy and communications (ICCEREC), pp 220–225

  80. Jusoh LI, Sulaiman E, Omar MF, Soomro HA (2018) A comparative studyof single-tooth and multi-tooth stator of 4S–8P permanent magnet FSM for electric bicycle application. Int J Eng Technol 7(4.3):295–298

    Google Scholar 

  81. Kumar R, Sulaiman E, Soomro HA, Jusoh LI, Bahrim FS, Omar MF (2017) Design enhancement and performance examination of external rotor switched flux permanent magnet machine for downhole application. IOP Conf Ser Mater Sci Eng 226(1):012125

    Google Scholar 

  82. Sulaiman E, Kosaka T, Matsui N (2011) Design optimization of 12Slot-10Polehybrid excitation flux switching synchronous machine with 0.4kg permanentmagnet for hybrid electric vehicles. In: 8th international conference on power electronics—ECCE Asia, pp 1913–1920

  83. Sulaiman E, Kosaka T, Matsui N (2011) A novel hybrid excitation fluxswitching synchronous machine for a high-speed hybrid electric vehicleapplications. Int Conf Electr Mach Syst 2011:1–6

    Google Scholar 

  84. Ahmad MZ, Sulaiman E, Kosaka T (2015) Optimization of outer-rotor hybridexcitation FSM for in-wheel direct drive electric vehicle. In: IEEE international conference on mechatronics (ICM), pp 691–696

  85. Ahmad MZ, Sulaiman E, Kosaka T (2015) Analysis of high torque and powerdensities outer-rotor PMFSM with DC excitation coil for in-wheel direct drive. J Magn 20(3):265–272

    Google Scholar 

  86. Ahmad MZ, Sulaiman E, Haron ZA, Khan F (2014) FEA-based design study of 12-slot 14-pole outer-rotor dual excitation flux switching machinefor direct drive electric vehicle applications. Appl Mech Mater 660(1):836–840

    Google Scholar 

  87. Mazlan MMA, Sulaiman E, Ahmad MZ, Othman SMNS (2014) Designoptimization of single-phase outer-rotor hybrid excitation flux switching motorfor electric vehicles. In: IEEE student conference on research and development (SCOReD), pp 1–6

  88. Sulaiman E, Zakaria SNU, Kosaka T (2015) Parameter sensitivity study foroptimization of single phase E-Core hybrid excitation flux switching machine. In: IEEE international conference on mechatronics (ICM), pp 697–702

  89. Khan F, Sulaiman E (2015) Design optimization and efficiency analysis of12slot-10pole wound field flux switching machine. In: IEEE magnetics conference (INTERMAG), pp 1–1

  90. Khan F, Sulaiman E, Ahmad MZ, Ali H (2014) Design refinement andperformance analysis of 12 slot-8 pole wound field salient rotor switched flux machine for hybrid electric vehicles. In: 12th International conferenceon frontiers of information technology, pp 197–201

  91. Sulaiman E, Khan F, Omar MF, Romalan GM, Jenal M (2016) Optimaldesign of wound-field flux switching machines for an all-electric boat. In: XXII international conference on electrical machines (ICEM), pp 2464–2470

  92. Jenal M, Sulaiman E, Ahmad MZ, Khan F, Omar MF (2016) A newalternate circumferential and radial flux (AlCiRaF) permanent magnet fluxswitching machine for light weight EV. In: XXII international conference onelectrical machines (ICEM), pp 2399–2405

  93. Enwelum MI, Sulaiman EB, Khan F (2016) Optimization of 12S-14P permanent magnet flux switching motor (PMFSM) for electric scooter application. In: 4th IET clean energy and technology conference (CEAT2016), pp 1–6

  94. Jenal M, Sulaiman E, Ahmad MZ, Khan F, Omar MF (2016) A newalternate circumferential and radial flux (AlCiRaF) permanent magnet fluxswitching machine for light weight EV. XXII Int Conf Electr Mach (ICEM) 21(4):2399–2405

    Google Scholar 

  95. Kumar R, Sulaiman E, Ahmad MZ, Othman SMNS, Amin F (2017) Comparative study of initial and optimal outer rotor permanent magnet fluxswitching machine for downhole application. In: First internationalconference on latest trends in electrical engineering and computingtechnologies (INTELLECT), pp 1–6

  96. Jusoh LI, Sulaiman E (2018) Analysis and performance of 4S-8P permanentmagnet flux switching motors (PMFSM) for electric bicycle applications. In: 5th IET international conference on clean energy and technology (CEAT2018), pp 1–7

  97. Vesterstrom J, Thomsen RA (2004) Comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numericalbenchmark problems. In: Proceedings of the 2004 congress on evolutionarycomputation (IEEE Cat. No.04TH8753), pp 1980–1987

  98. Hegerty B, Hung C, Kasprak K (2009) A comparative study on differential evolution and genetic algorithms for some combinatorial problems. In: Proceedings of the 8th Mexican international conference on artificialintelligence, pp 1–13

  99. Huifen Lu, Yunyue Ye, Ruojun J (2001) Improved Tabu method applied to electromagnetic device designs. Proc Fifth Int Conf Electr Mach Syst (ICEMS) 1(1):257–260

    Google Scholar 

  100. Bramerdorfer G, Tapia JA, Pyrhonen JJ, Cavagnino A (2018) Modern electrical machine design optimization: techniques, trends, and best practices. IEEE Trans Ind Electron 65(10):7672–7684

    Google Scholar 

  101. Li Y, Bobba D, Sarlioglu B (2018) Design and optimization of a novel dualrotor hybrid PM machine for traction application. IEEE Trans Ind Electron 65(2):1762–1771

    Google Scholar 

  102. Lim D-K, Jung S-Y, Yi K-P, Jung H-K (2018) A novel sequential-stageoptimization strategy for an interior permanent magnet synchronousgenerator design. IEEE Trans Ind Electron 65(2):1781–1790

    Google Scholar 

  103. Wang Q, Niu S, Yang S (2017) Design optimization and comparative studyof novel magnetic-geared permanent magnet machines. IEEE Trans Magn 53(6):1–4

    Google Scholar 

  104. Zhao X, Niu S (2017) Design and optimization of a new magnetic-geared polechanging hybrid excitation machine. IEEE Trans Ind Electron 64(12):9943–9952

    Google Scholar 

  105. Han W, Tran TT, Kim JW, Kim YJ, Jung SY (2016) Mass ionized particle optimization algorithm applied to optimal FEA-based design of electric machine. IEEE Trans Magn 52(3):1–4

    Google Scholar 

  106. Yang L, Ho SL, Fu WN (2014) Design optimizations of electromagnetic devices using sensitivity analysis and Tabu algorithm. IEEE Trans Magn 50(11):1–4. https://doi.org/10.1109/TMAG.2014.2322625

    Article  Google Scholar 

  107. Lee JH, Song JY, Kim DW, Kim JW, Kim YJ, Jung SY (2017) Particle swarm optimization algorithm with intelligent particle number control for optimal design of electric machines. IEEE Trans Ind Electron 65(2):1791–1798. https://doi.org/10.1109/TIE.2017.2760838

    Article  Google Scholar 

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Acknowledgements

This research is entirely supported by ‘‘Research and Management Center, Universiti Tun Hussein Onn Malaysia (UTHM) through TIER1 (Vot H755) and Ministry of Higher Education (MOHE) through Fundamental Research Grant Scheme (FRGS-RACER Vot RACER/1/2019/TK07/UTHM//1), respectively.

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  1. Research Center for Applied Electromagnetics (EMCenter), Institute of Integrated Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400, Parit Raja, Batu Pahat, Johor, Malaysia

    Mohd. Fairoz Bin Omar, Erwan Bin Sulaiman, Irfan Ali Soomro, Md. Zarafi Bin Ahmad & Roziah Aziz

  2. Electrical Engineering Department, Quaid-E-Awam University of Engineering, Science and Technology, Nawabshah, Sindh, Pakistan

    Irfan Ali Soomro

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Omar, M.F.B., Sulaiman, E.B., Soomro, I.A. et al. Design Optimization Methods for Electrical Machines: A Review. J. Electr. Eng. Technol. 18, 2783–2800 (2023). https://doi.org/10.1007/s42835-022-01358-y

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