access icon free Multi-objective optimisation design and performance comparison of permanent magnet synchronous motor for EVs based on FEA

The requirements of high efficiency, power density, and low price for the motor of electric vehicles (EVs) make the design of the driving motor become a process of multi-objective optimisation. For purpose of the permanent magnet synchronous motor (PMSM) used for EVs has the higher efficiency, wider range of speed regulation with flux-weakening and better cost superiority, a multi-objective optimisation design approach based on finite element analysis (FEA) and modified particle swarm optimisation (MPSO) algorithm which takes efficiency, flux-weakening rate, and price as optimisation objectives is proposed in this study. Five PMSMs with different rotor topologies (V-shape, U-shape, double V-shape, delta-shape, and double tangential-shape) are optimised by the proposed optimisation method and their performance characteristics, including flux-weakening ability, efficiency, price, and anti-demagnetisation ability, are compared. The results suggest that double V-shape rotor topology has the wider constant power range and double-layer PMs topology has stronger anti-demagnetisation ability and wider high efficiency interval, whereas single-layer topology has lower cost price. Furthermore, a PMSM prototype with V-shape PMs is manufactured, so that the feasibility of multi-objective optimisation design approach and accuracy of FEA are verified by prototype experiments.

Inspec keywords: rotors; magnetic flux; machine theory; finite element analysis; permanent magnet motors; particle swarm optimisation; electric vehicles; synchronous motors

Other keywords: modified particle swarm optimisation algorithm; flux-weakening rate; anti-demagnetisation ability; electric vehicles motor; finite element analysis; multiobjective optimisation design approach; PMSM; FEA; V-shape PM; permanent magnet synchronous motor; double V-shape rotor topology

Subjects: Synchronous machines; Transportation; Finite element analysis; Optimisation techniques

References

    1. 1)
      • 25. Lee, J.H., Song, J.-Y., Kim, D.-W., et al: ‘Particle swarm optimization algorithm with intelligent particle number control for optimal design of electric machines’, IEEE Trans. Ind. Electron., 2018, 65, (2), pp. 17911798.
    2. 2)
      • 28. Sudhakar Babu, T., Rajasekar, N., Sangeetha, K.: ‘Modified particle swarm optimization technique based maximum power point tracking for uniform and under partial shading condition’, Appl. Soft Comput., 2015, 34, pp. 613624.
    3. 3)
      • 34. Tang, R.: ‘Modern permanent magnet machines theory and design’ (Mechanical Industrial Press, Beijing, 2015).
    4. 4)
      • 9. Aydin, E., Li, Y., Aydin, I., et al: ‘Minimization of torque ripples of interior permanent magnet synchronous motors by particle swarm optimization technique’. IEEE ITEC, Dearborn, 2015, pp. 16.
    5. 5)
      • 18. Krasopoulos, C.T., Beniakar, M.E., Kladas, A.G.: ‘Multicriteria PM motor design based on ANFIS evaluation of EV driving cycle efficiency’, IEEE Trans. Trans. Electr., 2018, 4, (2), pp. 525535.
    6. 6)
      • 7. Jingjuan, D., Xiaoyuan, W., Haiying, L.: ‘Optimization of magnet shape based on efficiency map of IPMSM for EVs’, IEEE Trans. Appl. Supercon., 2016, 26, (7), pp. 17.
    7. 7)
      • 31. Tessarolo, A., Mezzarobba, M., Menis, R.: ‘Modeling, analysis, and testing of a novel spoke-type interior permanent magnet motor with improved flux weakening capability’, IEEE Trans. Magn., 2015, 51, (4), pp. 110.
    8. 8)
      • 26. Akay, B., Karaboga, D.: ‘A modified artificial Bee colony algorithm for real-parameter optimization’, Inf. Sci., 2012, 192, pp. 120142.
    9. 9)
      • 33. Zhang, B., Qu, R., Wang, J., et al: ‘Thermal model of totally enclosed water-cooled permanent-magnet synchronous machines for electric vehicle application’, IEEE Trans. Ind. Appl., 2015, 51, (4), pp. 30203029.
    10. 10)
      • 19. Zhu, J., Li, S., Song, D., et al: ‘Multi-objective optimisation design of air-cored axial flux PM generator’, IET Electr. Power Appl., 2018, 12, (9), pp. 13901395.
    11. 11)
      • 16. Smaka, S., Konjicija, S., Masic, S., et al: ‘Multi-objective design optimization of 8/14 switched reluctance motor’. IEEE ITMDC, IL, 2013, pp. 468475.
    12. 12)
      • 4. Chae-Lim, J., Jin, J.: ‘Optimization design of PMSM with hybrid-type permanent magnet considering irreversible demagnetization’, IEEE Trans. Magn., 2017, 53, (11), pp. 14.
    13. 13)
      • 12. Bonthu, S.S.R., Choi, S., Baek, J.: ‘Design optimization with multiphysics analysis on external rotor permanent magnet-assisted synchronous reluctance motors’, IEEE Trans. Energy Convers., 2018, 33, (1), pp. 290298.
    14. 14)
      • 22. Kaboli, S., Fallahpour, A., Selvaraj, J., et al: ‘Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming’, Energy, 2017, 126, pp. 144164.
    15. 15)
      • 1. Dajaku, G., Hofmann, H., Hetemi, F., et al: ‘Comparison of two different ipm traction machines with concentrated winding’, IEEE Trans. Ind. Electron., 2016, 63, (7), pp. 41374149.
    16. 16)
      • 2. Alper, T., Liridon, X., Murat, Y., et al: ‘Comprehensive design and analysis of a PMaSynRM for washing machine applications’, IET Electr. Power Appl., 2018, 12, (9), pp. 13111319.
    17. 17)
      • 32. Zhang, B., Qu, R., Wang, J., et al: ‘Design and comparison of interior permanent magnet motor topologies for traction applications’, IEEE Trans. Trans. Electr., 2017, 3, (1), pp. 8697.
    18. 18)
      • 20. Chen, H., Yan, W., Gu, J.J., et al: ‘Multi-objective optimization design of a switched reluctance motor for low-speed electric vehicles with a taguchi–CSO algorithm’, IEEE/ASME Trans. Mech., 2018, 23, (4), pp. 17621774.
    19. 19)
      • 35. Zhang, K., Song, J., Ni, K., et al: ‘Lagrange interpolation learning particle swarm optimization’, PLOS ONE., 2016, 11, (4), p. e0154191.
    20. 20)
      • 5. Ahn, K., Bayrak, A., Papalambros, P.: ‘Electric vehicle design optimization: integration of a high-fidelity interior-permanent-magnet motor model’, IEEE Trans. Vehicular. Tech., 2015, 64, (9), pp. 38703877.
    21. 21)
      • 10. Dang, L., Bernard, N., Bracikowski, N., et al: ‘Analytical model and reluctance network for high-speed PMSM design optimization application to electric vehicless’. IEEE ICEM, Lausanne, 2016, pp. 13591365.
    22. 22)
      • 6. Vidanalage, B., Toulabi, M., Filizadeh, S.: ‘Electromagnetic design optimization and sensitivity analysis for IPM synchronous motors’. IEEE ICIT, Toronto, 2017, pp. 288293.
    23. 23)
      • 11. Dang, L., Bernard, N., Bracikowski, N., et al: ‘Design optimization with flux weakening of high-speed PMSM for electrical vehicle considering the driving cycle’, IEEE Trans. Ind. Electron., 2017, 64, (12), pp. 98349843.
    24. 24)
      • 29. Finken, T., Hombitzer, M., Hameyer, K.: ‘Study and comparison of several permanent-magnet excited rotor types regarding their applicability in electric vehicles’. IEEE EMOBTILITY, Leipzig, 2010, pp. 17.
    25. 25)
      • 14. Ilka, R., Alinejad-Beromi, Y., Yaghobi, H.: ‘Techno-economic design optimisation of an interior permanent-magnet synchronous motor by the multi-objective approach’, IET Electr. Power Appl., 2018, 12, (7), pp. 972978.
    26. 26)
      • 24. Li, W., Shi, X., Yang, J., et al: ‘Optimisation of non-uniform time-modulated conformal arrays using an improved non-dominated sorting genetic-II algorithm’, IET Microw., Antennas Propag., 2014, 8, (4), pp. 287294.
    27. 27)
      • 27. Wang, S., Watada, J.: ‘A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty’, Inf. Sci., 2012, 192, pp. 318.
    28. 28)
      • 30. Liu, X., Chen, H., Zhao, J., et al: ‘Research on the performances and parameters of interior PMSM used for electric vehicles’, IEEE Trans. Ind. Electron., 2016, 63, (6), pp. 35333545.
    29. 29)
      • 15. Lee, J.H., Kim, J., Song, J., et al: ‘A novel memetic algorithm using modified particle swarm optimization and mesh adaptive direct search for PMSM design’, IEEE Trans. Magn., 2016, 52, (3), pp. 14.
    30. 30)
      • 17. Parasiliti, F., Villani, M., Lucidi, S., et al: ‘Finite-element-based multiobjective design optimization procedure of interior permanent magnet synchronous motors for wide constant-power region operation’, IEEE Trans. on Ind. Electron., 2012, 59, (6), pp. 25032514.
    31. 31)
      • 23. Rafieerad, A.R., Bushroa, A.R., Nasiri-Tabrizi, B., et al: ‘Toward improved mechanical, tribological, corrosion and in-vitro bioactivity properties of mixed oxide nanotubes on Ti-6Al-7Nb implant using multi-objective PSO’, J. the Mech. Behav. Biomed. Mater., 2017, 69, pp. 118.
    32. 32)
      • 8. Guo, H., Tian, W., Xaiofeng, D.: ‘Multi-objective optimal design of permanent magnet synchronous motor for high efficiency and high dynamic performance’, IEEE Access., 2018, 6, pp. 2356823581.
    33. 33)
      • 3. Weiduo, Z., Xuejiao, W., Chris, G., et al: ‘Multi-physics and multi-objective optimization of a high speed pmsm for high performance applications’, IEEE Trans. Magn., 2018, 54, (1), pp. 15.
    34. 34)
      • 13. Öksüztepe, E.: ‘In-wheel switched reluctance motor design for electric vehicles by using a pareto-based multiobjective differential evolution algorithm’, IEEE Trans. Veh. Tech., 2017, 66, (6), pp. 47064715.
    35. 35)
      • 21. Ruba, M., Ruba, M., Martis, C., et al: ‘Synchronous reluctance machine geometry optimisation through a genetic algorithm based technique’, IET Electr. Power Appl., 2018, 12, (3), pp. 431438.
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