DESIGN OF THE PERMANENT MAGNET SYNCHRONOUS MOTOR USED IN ELECTRIC VEHICLES WITH THE HELP OF THE PARTICLE SWARM ALGORITHM AND ANSYS-MAXWELL

Year 2023, , 141 - 155, 31.08.2023

Yıldırım Özüpak , Mehmet Çınar

https://doi.org/10.54365/adyumbd.1315079

Cited By: 1

Abstract

Performance and efficiency are among the most important parameters in electric motors. Improvements to the engine not only increase engine performance, but also affect production costs. Permanent Magnet Synchronous Motor (PMSM) is a motor widely used in electric vehicles with its high torque density, high efficiency and production advantages. In this study, 110 kW (Adjust-Speed Permanent Magnet Synchronous Motor) AS-PMSM was designed for electric vehicle applications. The results of ANSYS-Maxwell and Particle Swarm Optimization (PSO) algorithms and the calculations of the optimized design were compared in line with the obtained data. In the design study, theoretical calculations, PSO algorithm and finite element method (FEM) based ANSYS-Maxwell software were used. Based on these values, the computer aided design of the engine was carried out. First of all, the basic design and performance criteria of the engine were determined. Parameters of the motor such as torque ripple, iron loss, efficiency and electromagnetic analysis were analyzed and optimized with ANSYS-Maxwell. Then, electrical, magnetic and performance analyzes of the motor were made. How the engine performance changes with the methods used has been examined in detail. Efficiency values were determined under various operating conditions in engine performance studies. The validity of the improvement study and the verification of the design study were examined by comparing the ANSYS results with the PSO algorithm results. The analysis results showed that the designed electric motor met the desired power values and design performance criteria.

Keywords

PMSM, FEM, Design, Efficiency, PSO

ELEKTRİKLİ ARAÇLARDA KULLANILAN KALICI MIKNATISLI SENKRON MOTORUN PARÇACIK SÜRÜ ALGORİTMASI VE ANSYS-MAXWELL YARDIMIYLA TASARIMI VE ANALİZİ

Abstract

Elektrik motorlarında performans ve verimlilik en önemli parametreler arasındadır. Motor üzerinde yapılacak iyileştirmeler sadece motor performansını artırmakla kalmıyor, aynı zamanda üretim maliyetlerine de etki ediyor. Kalıcı Mıknatıslı Senkron Motor (KMSM), yüksek tork yoğunluğu, yüksek verim ve üretim avantajları ile elektrikli araçlarda yaygın olarak kullanılan bir motordur. Bu çalışmada, elektrikli araç uygulamaları için 110 kW (Adjust-Speed Kalıcı Mıknatıslı Senkron Motor) AS-KMSM tasarımı yapılmıştır. ANSYS-Maxwell ve Parçacık Sürü Optimizasyonu (PSO) algoritması sonuçları ve optimize edilen tasarımın hesaplamaları, elde edilen veriler doğrultusunda karşılaştırılmıştır. Tasarım çalışmasında teorik hesaplamalar, PSO algoritması ve sonlu elemanlar yöntemi (SEY) tabanlı ANSYS-Maxwell yazılımı kullanılmıştır. Bu değerlere dayalı olarak motorun bilgisayar destekli tasarımı gerçekleştirilmiştir. Öncelikle motorun temel tasarım ve performans kriterleri belirlenmiştir. Motorun, tork dalgalanması, demir kaybı, verimliliği ve elektromanyetik analizi gibi parametreleri ANSYS-Maxwell ile analiz edilmiş ve optimize edilmiştir. Ardından motorun elektriksel, manyetik ve performans analizleri yapılmıştır. Kullanılan yöntemler ile motor performansının nasıl değiştiği detaylı olarak incelenmiştir. Motor performans çalışmalarında çeşitli çalışma koşulları altında verim değerleri belirlenmiştir. ANSYS sonuçları ile PSO algoritması sonuçları karşılaştırılarak iyileştirme çalışmasının geçerliliği ve tasarım çalışmasının doğrulanması incelenmiştir. Analiz sonuçları, tasarlanan elektrik motorunun istenen güç değerlerini ve tasarım performans kriterlerini karşıladığını göstermiştir.

Keywords

KMSM, SEY, Verimlilik, Tasarım, PSO

References

• Lee J.H., Kim D., Song J., Jung S., Kim Y., (2016) Design of 100kW Propulsion Motor for Electric Conversion Vehicle Based on Vehicle Driving Performance Simulation, IEEE Transportation Electrification Conference and Expo, Busan-Korea (South), 412-416, 1-4.
• Bouscayrol A., Boulon L., Hofman T., Chan C.C., (2016) Special Section on Advanced Powertrains for More Electric Vehicles, IEEE Transactions on Vehicular Technology, 6 (3), 995 – 997.
• Zhu S., Hu Y., Liu C., Wang K., (2018) Iron Loss and Efficiency Analysis of Interior PM Machines for Electric Vehicle Applications, IEEE Transactions on Industrial Electronics, 65 (1), 114-124.
• Özüpak Y. (2022) Efficiency Analysis of BLDC for variable magnetic field. 2022. MANAS Journal of Engineering, Volume 10 (Issue 1).
• A. Belmondo, F. Giuggioli, and B. Giorgi, (1983) Optimization of ferrographic oil analysis for diesel engine wear monitoring,” Wear, vol. 90, no. 1, pp. 49 – 61.
• J. J. Kim and H. Y. Kim, (1997) Shape design of an engine mount by a method of parameter optimization,” Computers & Structures, vol. 65, no. 5, pp. 725 – 731.
• C. Gagne, M. Gravel, and W. L. Price, (2006) Solving real car sequencing problems with ant colony optimization,” European Journal of Operational Research, vol. 174, no. 3, pp. 1427 – 1448, 2006.
• D. Lowe and K. Zapart, (1997) Validation of neural networks in automotive engine calibration,” in fifth International Conference on Artificial Neural Networks (Conf. Publ. No. 440), pp. 221–226.
• K. Zeng, S. Lv, B. Liu, F. Ma, and Z. Huang, (2006) Development and calibration on an electronic control system of cng engine,” in IEEE International Conference on Vehicular Electronics and Safety. ICVES, pp. 204–208.
• C. Vong, P. Wong, and H. Huang, (2009) Case-based reasoning for automotive engine electronic control unit calibration,” in Information and Automation, 2009. ICIA ’09. International Conference on, pp. 1380–1385.
• A. Rosato and S. Sibilio, (2012) Calibration and validation of a model for simulating thermal and electric performance of an internal combustion engine-based micro-cogeneration device,” Applied Thermal Engineering, vol. 45-46, pp. 79 – 98.
• L. Brzozowska, K. Brzozowski, and J. Nowakowski, (2005) An application of artificial neural network to diesel engine modelling,” in Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications. IDAACS 2005. IEEE, 2005, pp. 142–146.
• K. Atashkari, N. Nariman-Zadeh, M. Golcu, A. Khalkhali, and A. Jamali, (2007) Modelling and multi-objective optimization of a variable valvetiming spark-ignition engine using polynomial neural networks and evolutionary algorithms,” Energy Conversion and Management, vol. 48,no. 3, pp. 1029 – 1041.
• C. Hametner and S. Jakubek, (2011) Combustion engine modelling using an evolving local model network,” in Fuzzy Systems (FUZZ), IEEE International Conference on, 2011, pp. 2802–2807.
• Liu J., Gong C., Wu Z., (2017) Influence Research of Rotor Structure Parameters on the Performance of IPMSM, 20th International Conference on Electrical Machines and Systems (ICEMS), Sydney-Australia, 1-5, 11-14.
• Rakesh K., Sanjeevikumar P., (2019) Electric Vehicles for India: Overview and Challenges , IEEE India Info. Vol. 14 No. 2.
• Bozhidar S., George T., Plamen R., Gerasimos P., Leonidas D., (2017) Finite element analysis of rotating electrical machines- an educational approach‖, IEEE Global Engineering Education Conference (EDUCON).
• Chan-B. P., Geochul J., (2017) Design and Analysis of Magnetic-Geared Permanent Magnet Synchronous Motor for Driving Electric Vehicles‖, IEEE - International Conference on Electrical Machines and Systems (ICEMS).
• Emma A., G., Torbjörn T., (2016) Performance Analysis of Current BEVs Based on a Comprehensive Review of Specifications‖, IEEE transactions on transportation electrification, vol. 2, no. 3.
• Yamano, K., Morimoto, S., Sanada, M., Inoue Y., (2016) Basic Study on Design of Surface Permanent Magnet Synchronous Motor Using Design Assist System of PMSM‖, IEEE - International Conference on Electrical Machines and Systems (ICEMS).
• Yamazaki K, Kumagai M, Ikemi T and Ohki S., (2013) IEEE Transactions on Industry Applications vol 49 pp 2478–86
• Özüpak, Y., (2022) Investigation of the Effect of Design Parameters of Small Brushless DC Motors on Motor Performance by Finite Element Method, Brilliant Engineering, 3, 4658. https://doi.org/10.36937/ben.2022. 4658.
• Claudio B, Fabio I, Emilio L, Alberto B and Matteo D (2012) IEEE Transactions on Magnetics vol 48 pp 2685-93
• Federico Marini, Beata Walczak, (2015) Particle swarm optimization (PSO). A tutorial, Chemometrics and Intelligent Laboratory Systems, Volume 149, Part B, Pages 153-165, ISSN 0169-7439, https://doi.org/10.1016/j.chemolab.2015.08.020.
• M. Reyes-Sierra and C. A. C. Coello, (2006) Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art,” International Journal of Computational Intelligence Research, Vol. 2, No. 3, 2006, pp. 287-308.
• T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh and S. Mirjalili, (2002) Particle Swarm Optimization: A Comprehensive Survey, in IEEE Access, vol. 10, pp. 10031-10061, 2022, doi: 10.1109/ACCESS.2022.3142859