Elektrikli araçlar için farklı hız profilleri altında model öngörülü akım kontrollü İSMSM sürücüsünün başarımı

Yıl 2024, , 1478 - 1484, 15.10.2024

Rıdvan Demir

https://doi.org/10.28948/ngumuh.1538775

Öz

Elektrikli araçlar için, yük ve araç hızının şehir içi yol, kırsal yol, otoyol veya arazi yolları gibi gerçek trafik koşullarının tamamında nasıl değiştiğini kesin olarak modellemek kolay değildir. Bununla birlikte uluslararası geçerliliği olan bazı elektrikli araç sürüş döngüleri tipik trafik koşullarını modellemek için iyileştirilmiştir. Bu çalışmada, araçlar için iyileştirilen sürüş döngülerinden bazıları model öngörülü akım kontrol tabanlı elektrikli araç sürücüsüne hız referansı olarak uygulanmıştır. Ek olarak elektrikli aracın toplam çekiş kuvveti kullanılarak oluşturulan yük momenti elektrikli aracı tahrik eden iç yüzey sürekli mıknatıslı senkron motora uygulanmıştır. Farklı hız profilleri ve yük momenti şartları için model öngörülü akım kontrol tabanlı elektrikli araç sürücüsünün başarımı MATLAB Simulink ortamında test edilmiştir. Farklı yük momentleri altında ve sıfır hızı da içeren geniş bir hız aralığında elde edilen benzetim sonuçları elektrikli araçlar için model öngörülü akım kontrol tabanlı iç yüzey sürekli mıknatıslı senkron motor sürücüsünün başarımını onaylamaktadır.

Anahtar Kelimeler

Elektrikli araçlar, Model öngörülü akım kontrol, İç yüzey sürekli mıknatıslı senkron motor, AC motor sürücüsü

Performance of model predictive current controlled IPMSM drive under different speed profiles for electric vehicles

Öz

For electric vehicles, it is not easy to model exactly how the load and vehicle speed change in all real traffic conditions such as urban roads, rural roads, highways, or off-road. However, some internationally valid electric vehicle driving cycles have been improved to model typical traffic conditions. In this study, some of the enhanced driving cycles for vehicles are applied to the model predictive current control-based electric vehicle drive as the speed reference. In addition, the load torque generated by using the total traction force of the electric vehicle is applied to the interior permanent magnet synchronous motor driving the electric vehicle. The performance of the model predictive current control-based electric vehicle drive for different speed profiles and load torque conditions is tested in MATLAB Simulink environment. The simulation results obtained under different load torques and a wide speed range including zero speed confirm the performance of the model predictive current control based interior permanent magnet synchronous motor drive for electric vehicles.

Anahtar Kelimeler

Electrical vehicles, Model predictive current control, Interior permanent magnet synchronous motors, AC motor drive

Kaynakça

• I. F. Bouguenna, A. Tahour, R. Kennel, and M. Abdelrahem, Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems. Energies, 14(6), 1-23, 2021. https://doi.org/ 10.3390/en14061722.
• M. Toren and H. Mollahasanoğlu, Gömülü kalıcı mıknatıslı-fırçasız doğru akım motorda (IPMBLDC) kullanılan farklı güç dereceli NdFeB mıknatısların motor performansına etkisinin incelenmesi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 38(3), 1389-1402, 2023. https://doi.org/10.17341/ gazimmfd.988877.
• Z. Yang, F. Shang, I. P. Brown, and M. Krishnamurthy, Comparative Study of Interior Permanent Magnet, Induction, and Switched Reluctance Motor Drives for EV and HEV Applications. IEEE Transactions on Transportation Electrification, 1(3), 245–254, 2015. https://doi.org/10.1109/TTE.2015.2470092.
• K. Diao, X. Sun, G. Lei, G. Bramerdorfer, Y. Guo, and J. Zhu, System-Level Robust Design Optimization of a Switched Reluctance Motor Drive System Considering Multiple Driving Cycles. IEEE Transactions on Energy Conversion, 36(1), 348–357, 2021. https://doi.org/ 10.1109/TEC.2020.3009408.
• L. Chen, H. Xu, and X. Sun, A Novel Strategy of Control Performance Improvement for Six-Phase Permanent Magnet Synchronous Hub Motor Drives of EVs Under New European Driving Cycle. IEEE Transactions on Vehicular Technology, 70(6), 5628–5637, 2021. https://doi.org/10.1109/TVT.2021.3079 576.
• H. Chaoui, M. Khayamy, and O. Okoye, Adaptive RBF Network Based Direct Voltage Control for Interior PMSM Based Vehicles. IEEE Transactions on Vehicular Technology, 67 (7), 5740–5749, 2018, https://doi.org/10.1109/TVT.2018.2813666.
• O. C. Kivanc and S. B. Ozturk, Sensorless PMSM Drive Based on Stator Feedforward Voltage Estimation Improved With MRAS Multiparameter Estimation. IEEE/ASME Transactions on Mechatronics, 23(3), 1326–1337, 2018, https://doi.org/10.1109/TMECH.2 018.2817246.
• S. Abareshi, S. Tohidi, M. B. Bannae Sharifian, and A. Younesi, Model predictive control by combining vectors for surface and interior permanent-magnet synchronous motor. International Transactions on Electrical Energy Systems, 31(8), e12959, 2021. https://doi.org/10.1002/2050-7038.12959.
• A. A. Ahmed, M. M. Akl, and E. E. M. Rashad, A comparative dynamic analysis between model predictive torque control and field-oriented torque control of IM drives for electric vehicles, International Transactions on Electrical Energy Systems, 31 (11), e13089, 2021. https://doi.org/10.1002/2050-7038.1308 9.
• O. Sandre-Hernandez, J. Rangel-Magdaleno, and R. Morales-Caporal, A Comparison on Finite-Set Model Predictive Torque Control Schemes for PMSMs. IEEE Transactions on Power Electronics, 33(10), 8838–8847, 2018. https://doi.org/10.1109/TPEL.2017.2777973
• Y. Zhang, D. Xu, J. Liu, S. Gao, and W. Xu, Performance Improvement of Model-Predictive Current Control of Permanent Magnet Synchronous Motor Drives. IEEE Transactions on Industry Applications, 53(4), 3683–3695, 2017. https://doi.org/10.1109/ TIA.2017.2690998.
• X. Zhang, L. Zhang, and Y. Zhang, Model Predictive Current Control for PMSM Drives With Parameter Robustness Improvement. IEEE Transactions on Power Electronics, 34(2), 1645–1657, 2019. https://doi.org/ 10.1109/TPEL.2018.2835835.
• S. Fan and C. Tong, Model predictive current control method for PMSM drives based on an improved prediction model. Journal of Power Electronics, 20(6), 1456–1466, 2020. https://doi.org/10.1007/s43236-020-00125-4.
• S. G. Petkar, K. Eshwar, and V. K. Thippiripati, A Modified Model Predictive Current Control of Permanent Magnet Synchronous Motor Drive. IEEE Transactions on Industrial Electronics, 68(2), 1025–1034, 2021. https://doi.org/10.1109/TIE.2020.2970671.
• X. Jiang et al., An Improved Implicit Model Predictive Current Control With Continuous Control Set for PMSM Drives. IEEE Transactions on Transportation Electrification, 8(2), 2444–2455 2022. https://doi.org /10.1109/TTE.2022.3144667.
• A. O. Kıyaklı and H. Solmaz, Modeling of an Electric Vehicle with MATLAB/Simulink. International Journal of Automotive Science And Technology, 2(4), 9–15, 2018. https://doi.org/10.30939/ijastech..475477.
• V.P., Dhote, M.M. Lokhande, and S.C. Gupta., Test bench setup for emulating electric vehicle on-road conditions, Energy Reports, 9, 218–227, 2023. https://doi.org/10.1016/j.egyr.2023.05.145
• C. P. Sahwal, S. Sengupta, and T. Q. Dinh, Advanced Equivalent Consumption Minimization Strategy for Fuel Cell Hybrid Electric Vehicles. Journal of Cleaner Production, 437, 1-14, 2024. https://doi.org/10.1016/ j.jclepro.2023.140366.
• M. Alzayed and H. Chaoui, Efficient Simplified Current Sensorless Dynamic Direct Voltage MTPA of Interior PMSM for Electric Vehicles Operation. IEEE Transactions on Vehicular Technology, 71(12) 12701–12710, 2022. https://doi.org/10.1109/ TVT.2022.3198095.
• C. T. Krasopoulos, M. E. Beniakar, and A. G. Kladas, Velocity and Torque Limit Profile Optimization of Electric Vehicle Including Limited Overload. IEEE Transactions on Industry Applications, 53(4), 3907–3916, 2017. https://doi.org/10.1109/TIA.2017.2680405
• R. Demir, Speed-sensorless Predictive Current Controlled PMSM Drive With Adaptive Filtering-based MRAS Speed Estimators, International Journal of Control, Automation and Systems, 21(8), 2577–2586, 2023. https://doi.org/10.1007/s12555-022-0698-z.
• MathWorks Inc., MATLAB 2023a/Simulink. Simulation and Model Based Design. MA, USA: Natick. (2023).
• X. Sun, Y. Zhang, X. Tian, J. Cao, and J. Zhu, ‘Speed Sensorless Control for IPMSMs Using a Modified MRAS With Gray Wolf Optimization Algorithm’, IEEE Transactions on Transportation Electrification, 8(1),1326–1337, 2022. https://doi.org/10.1109/TTE. 2021.3093580.