Elektrikli Araç için MÖK Tabanlı Akı Zayıflatma Kontrolü ile Asenkron Motorun Kayan Modlu Hız Tahmini

Year 2024, , 1403 - 1411, 02.12.2024

Barış Çavuş , Mustafa Aktaş

https://doi.org/10.35414/akufemubid.1472301

Abstract

Elektrikli araçlarda kullanılan motorların nominal hızın üzerinde çalıştırılması çok önemlidir. Elektrik motorlarının çok yüksek hızda çalıştırılabilmesi için akının kontrollü bir şekilde azaltılması gerekmektedir. Bu çalışmada akışı azaltmak için Model Öngörülü Kontrol (MÖK) yaklaşımı kullanılmıştır. Akı zayıflama bölgesinde hız sensörsüz asenkron motorun performansı değerlendirilmiştir. Çalışma sonucunda hem MÖK tabanlı akı zayıflatma kontrolünün hız sensörsüz performansı incelenmiş hem de Model Referans Adaptif Sistem (MRAS) ve Kayan Kipli Gözlemcinin (KKG) nominal değerin üzerindeki hız bölgesinde performansı değerlendirildi. Benzetim çalışmasıyla elde edilen akım, hız, akı ve moment verilerine dayanarak iki hız gözlemcisi ile de MPC tabanlı akı zayıflama kontrolünün sağlanabileceği başarıyla gösterilmiştir. Ayrıca çalışma sonucunda KKG'nin daha iyi sonuçlar verdiği görülmüştür.

Keywords

Model öngörülü kontrol, Akı zayıflama, Model referans adaptif sistem, Kayan kipli gözlemci, Doğrudan moment kontrolü

Sliding Mode Speed Estimation of Induction Motor with MPC Based Flux Weakening Control for Electric Vehicle

Abstract

It is very important to operate the motors used in electric vehicles above the rated speed. In order to operate electric motors with very high speed, the flux must be decreased in a controlled way. The Model Predictive Control (MPC) approach was employed in this study to reduce the flow. In the flux weakening area, the induction motor's performance without a speed sensor has been evaluated. As a result of the study, both the speed sensorless performance of the MPC based flux weakening control has been examined and the performance of the Model Reference Adaptive System (MRAS) and the Sliding Mode Observer (SMO) in very high speed region over nominal value has been evaluated. It has been successfully demonstrated that MPC-based flux weakening control with two speed observers can be achieved based on the current, speed, flux, and torque data collected through the simulation study. In addition, as a result of the study, it is seen that SMO has given better results.

Keywords

Model predictive control, Flux weakening, Model reference adaptive system, Sliding mode observer, Direct torque control

References

• Alfaro C., Guzman R., de Vicuña L.G., Miret J., Castilla M., 2021. Dual-Loop Continuous Control Set Model-Predictive Control for a Three-Phase Unity Power Factor Rectifier. IEEE Transactions on Power Electronics, 37(2), 1447-1460. https:/doi.org 10.1109/TPEL.2021.3107221
• Alsofyani I.M. and Idris N.R.N., 2015. Simple flux regulation for improving state estimation at very low and zero speed of a speed sensorless direct torque control of an induction motor. IEEE Transactions on Power Electronics, 31(4), 3027-3035. https:/doi.org 10.1109/TPEL.2015.2447731
• Ammar A., Benakcha A. and Bourek A., 2017. Adaptive MRAC-based direct torque control with SVM for sensorless induction motor using adaptive observer. The International Journal of Advanced Manufacturing Technology, 91(5), 1631-1641. https:/doi.org 10.1007/s00170-016-9840-5
• Ammar A., Kheldoun A., Metidji B., Ameid T. and Azzoug Y., 2020. Feedback linearization based sensorless direct torque control using stator flux MRAS-sliding mode observer for induction motor drive. ISA Transactions 98, 382-392. https://doi.org/ 10.1016/j.isatra.2019.08.061
• Basar M.S., Bech M.M., Andersen T.O., Scavenius P. and Thomas-Basar T., 2013. Comparison of sensorless FOC and SVM-DTFC of PMSM for low-speed applications. Proceedings of 4th International Conference on Power Engineering, Energy and Electrical Drives. Istanbul, Türkiye, 864-869.
• Chen N., Zheng Z., Zhou J., Li Y. and Wang K., 2013. A novel MPC flux weakening method for induction motor applied in electric wheel. Proceedings of 2013 International Conference on Electrical Machines and Systems (ICEMS). Busan, South Korea, 113-118.
• De Santiago J., Bernhoff H., Ekergård B., Eriksson S., Ferhatovic S., Waters R. and Leijon M., 2011. Electrical motor drivelines in commercial all-electric vehicles: A review. IEEE Transactions on vehicular Technology 61(2), 475-484. https:/doi.org 10.1109/TVT.2011.2177873
• Dias C.G. and Da Silva L.C., 2022. Induction Motor Speed Estimation Based on Airgap Flux Measurement Using Hilbert Transform and Fast Fourier Transform. IEEE Sensors Journal, 22(13), 12690 - 12699. https:/doi.org 10.1109/JSEN.2022.3176085
• Douiri M.R. and Cherkaoui M., 2013. Comparative study of various artificial intelligence approaches applied to direct torque control of induction motor drives. Frontiers in Energy 7(4), 456-467. https://doi.org/10.1007/s11708-013-0264-8
• Emiroğlu S., 2023. Parameter Estimation of Induction Motors using Hybrid GWO-CS Algorithm. Sakarya University Journal of Science, 27(2), 361-369. https://doi.org/10.16984/saufenbilder.1175899
• Farasat, Mehdi, Andrzej M Trzynadlowski and Mohammed Sami Fadali, 2014. Efficiency improved sensorless control scheme for electric vehicle induction motors, IET Electrical Systems in Transportation, 4(4), 122-31. https://doi.org/10.1049/iet-est.2014.0018
• Global E (2023) Global EV Outlook 2023: Catching up With Climate Ambitions.
• Gómez‐Peñate S., Valencia‐Palomo G., López‐Estrada F.R., Astorga‐Zaragoza C.M., Osornio‐Rios R.A., Santos‐Ruiz I., 2019. Sensor fault diagnosis based on a sliding mode and unknown input observer for Takagi‐Sugeno systems with uncertain premise variables. Asian Journal of Control, 21(1), 339-353. https://doi.org/10.1002/asjc.1913
• Gülbudak O. and Gökdağ M., 2022. Performance evaluation of model predictive control method for neutral point clamped inverter. Turkish Journal of Engineering, 6(3), 245-250. https://doi.org/10.31127/tuje.962857
• Jeong I.W., Choi W. S. and Park K. H., 2014. Sensorless vector control of induction motors for wind energy applications using MRAS and ASO. Journal of Electrical Engineering and Technology, 9(3), 873-881. https://doi.org/10.5370/JEET.2014.9.3.873
• Mishra, Saurabh, Anshul Varshney, Bhim Singh, and Hina Parveen, 2022. Driving-cycle-based modeling and control of solar-battery-fed reluctance synchronous motor drive for light electric vehicle with energy regeneration, IEEE Transactions on Industry Applications, 58(5), 6666-75. https://doi.org/10.1109/TIA.2022.3181224
• Paicu M., Boldea I., Andreescu G.D. and Blaabjerg F., 2009. Very low speed performance of active flux based sensorless control: interior permanent magnet synchronous motor vector control versus direct torque and flux control. IET electric power applications 3(6), 551-561. https://doi.org/ 10.1049/iet-epa.2008.0290
• Quintero-Manríquez, Eduardo, Edgar N Sanchez, M Elena Antonio-Toledo, and Flavio Muñoz, 2021. Neural control of an induction motor with regenerative braking as electric vehicle architecture. Engineering Applications of Artificial Intelligence, 104, 1-14. https://doi.org/10.1016/j.engappai.2021.104275
• Ren Y. and Zhu Z.Q., 2014. Enhancement of steady-state performance in direct-torque-controlled dual three-phase permanent-magnet synchronous machine drives with modified switching table. IEEE Transactions on Industrial Electronics 62(6), 3338-3350. https://doi.org/ 10.1109/TIE.2014.2376881
• Rezgui S., Mehdi A., Legrioui S., Meddouce H., Boulahia A. and Benalla H., 2013. IRFOC vs DTC performance comparison analysis. Proceedings of 2013 3rd International Conference on Electric Power and Energy Conversion Systems. Istanbul, Türkiye, 1-6.
• Riba J.R., López-Torres C., Romeral L. and Garcia A., 2016. Rare-earth-free propulsion motors for electric vehicles: A technology review. Renewable and Sustainable Energy Reviews 57, 367-379. https://doi.org/10.1016/j.rser.2015.12.121
• Rubino S., Bojoi R., Odhano S.A. and Zanchetta P., 2018. Model predictive direct flux vector control of multi-three-phase induction motor drives. IEEE Transactions on Industry Applications 54(5), 4394-4404. https://doi.org/10.1109/TIA.2018.2829458
• Sengamalai U., Anbazhagan G., Thamizh T.T., Vishnuram P., Khurshaid T. and Kamel S., 2022. Three Phase Induction Motor Drive: A Systematic Review on Dynamic Modeling, Parameter Estimation, and Control Schemes. Energies 15(21), 1-39. https://doi.org/10.3390/en15218260
• Su D., Zhang C. and Dong Y., 2017. An improved continuous-time model predictive control of permanent magnetic synchronous motors for a wide-speed range. Energies 10(12), 1-18. https://doi.org/10.3390/en10122051
• Wang Y., Shi Y., Xu Y. and Lorenz R.D., 2015. A comparative overview of indirect field oriented control (IFOC) and deadbeat-direct torque and flux control (DB-DTFC) for AC Motor Drives. Chinese Journal of Electrical Engineering 1(1), 9-20. https://doi.org/ 10.23919/CJEE.2015.7933134
• Ye S., 2019. Fuzzy sliding mode observer with dual SOGI-FLL in sensorless control of PMSM drives. Isa Transactions 85, 161-176. https://doi.org/ 10.1016/j.isatra.2018.10.004
• Zhang Y. and Qi R., 2022. Flux-weakening drive for IPMSM based on model predictive control. Energies 15(7), 1-14. https://doi.org/10.3390/en15072543