SavedAdd your journal

Journals / Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji / 2026 / Cilt: 13 Sayı: 3

Speed-Sensorless Direct Torque Controlled Brushless Direct Current Motor Drive Using OC-LMS Based MRAS Estimator for Light Electric Vehicles

Hafif Elektrikli Araçlar için OC-LMS Tabanlı MUS Kestiricisi Kullanan Hız-Algılayıcısız Doğrudan Moment Kontrollü Fırçasız Doğru Akım Motor Sürücüsü

Mehmet Ünal(KAYSERİ ÜNİVERSİTESİ, MÜHENDİSLİK, MİMARLIK VE TASARIM FAKÜLTESİ, ELEKTRİK-ELEKTRONİK MÜHENDİSLİĞİ BÖLÜMÜ)0009-0000-1376-9386
Rıdvan Demir(Kayseri Üniversitesi Mühendislik, Mimarlık ve Tasarım Fakültesi, Elektrik Elektronik Mühendisliği Bölümü)0000-0001-6509-9169
Engin Cemal Mengüç(Kayseri Üniversitesi, Mühendislik Mimarlık ve Tasarım Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü)0000-0002-0619-549X
Remzi İnan(ISPARTA UYGULAMALI BİLİMLER ÜNİVERSİTESİ TEKNOLOJİ FAKÜLTESİ)0000-0003-1717-3875
Pages
991–1001
Publication Date
2025-09-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Also, the execution times of the proposed OC-LMS-based MUS are compared with the traditional LMS-based MUS estimator, showing that the proposed method reduces the computational burden.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, a low-computational-burden least mean square (LMS)- based model reference adaptive system (MRAS) with an online censoring (OC) strategy for speed estimation of brushless direct current (BLDC) motor is proposed.
📊
Key Findings & Data:The performance of the designed estimator is tested with a challenging scenario, in which the Urban Dynamometer Driving Schedule (UDDS) driving profile is applied to the DTC-based driver as reference speed information.
💡
Conclusion & Impact:Also, the execution times of the proposed OC-LMS-based MUS are compared with the traditional LMS-based MUS estimator, showing that the proposed method reduces the computational burden.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:FDAM · Hafif Elektrikli Araçlar · Doğrudan Moment Kontrol

Abstract

In this study, a low-computational-burden least mean square (LMS)- based model reference adaptive system (MRAS) with an online censoring (OC) strategy for speed estimation of brushless direct current (BLDC) motor is proposed. The proposed OC-LMS-based MRAS speed estimator is validated on a speed-sensorless direct torque controlled (DTC) BLDC motor drive for different censoring ratios. The performance of the designed estimator is tested with a challenging scenario, in which the Urban Dynamometer Driving Schedule (UDDS) driving profile is applied to the DTC-based driver as reference speed information. Additionally, the load moment changes obtained by converting the total traction force obtained from the electric vehicle (EV) model are applied as a mechanical load to the BLDC motor. The results obtained from the simulation studies, including the performance tests realized in a wide speed range, including zero speed cases, verify the performance of both the OC-LMS-based MRAS estimator and the DTC-based BLDC motor driver. Also, the execution times of the proposed OC-LMS-based MUS are compared with the traditional LMS-based MUS estimator, showing that the proposed method reduces the computational burden.

Özet

Bu çalışmada, fırçasız doğru akım motorların (FDAM’lerin) hız kestirimi için düşük işlem yüküne sahip çevrimiçi sansürleme (online censoring-OC) en küçük ortalama kare (least mean square-LMS) temelli modele uyarlamalı sistem (MUS) önerilmiştir. Önerilen OC-LMS temelli MUS hız kestiricisi farklı sansürleme oranları için hız-algılayıcısız doğrudan moment kontrol (DMK) tabanlı FDAM sürücüsü ile doğrulanmıştır. Tasarlanan yeni kestiricinin başarımı kentsel dinamometre sürüş programı (Urban Dynamometer Driving Schedule-UDDS) sürüş profilinin DMK tabanlı sürücüye referans hız bilgisi olarak verildiği zorlayıcı senaryo ile test edilmiştir. Ek olarak, elektrikli araç (EA) modelinden elde edilen toplam çekiş kuvvetinin dönüştürülmesi ile elde edilen yük momenti değişimleri FDAM’ye yük olarak uygulanmıştır. Sıfır hız durumlarını da içeren geniş hız aralığı için gerçekleştirilen benzetim çalışmalarından elde edilen sonuçlar, hem OC-LMS temelli MUS kestiricisinin hem de bu kestiriciyi kullanan DMK tabanlı FDAM sürücüsünün başarımını doğrulamaktadır. Ayrıca önerilen OC-LMS temelli MUS’un işlem süreleri geleneksel LMS temelli MUS kestirici ile karşılaştırılmış ve önerilen yöntemin işlem yükünü düşürdüğü gösterilmiştir.

🔖

Cite This Article

Ünal, M., Demir, R., Mengüç, E. C., İnan, R. (2025). Speed-Sensorless Direct Torque Controlled Brushless Direct Current Motor Drive Using OC-LMS Based MRAS Estimator for Light Electric Vehicles. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, Cilt: 13 Sayı: 3, 991–1001. https://doi.org/10.29109/gujsc.1687368

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (23)

  1. Bouguenna IF, Tahour A, Kennel R, Abdelrahem M. Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems. Energies. 2021; 14: 1-23.
  2. Irimia ND, Lazar FI, Luchian M. Comparison Between Sinusoidal and Space Vector Modulation Techniques on the Resulting Electromagnetic Torque Ripple Produced by a Three-Phase BLDC Motor under Field-Oriented Control. 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). 2019; 640–645.
  3. Kokundu S, Aydemir MT. UVDGM Tabanlı AYK Yöntemiyle Fırçasız Doğru Akım Motorunun Konum Kontrolü. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji. 2020; 8:1, 1-14.
  4. Inan R. An Improved Model Predictive Current Control of BLDC Motor With a Novel Adaptive Extended Kalman Filter–Based Back EMF Estimator and a New Commutation Duration Approach for Electrical Vehicle. International Journal of Circuit Theory and Applications. 2025; 53: 2, 1135–1150.
  5. Ubare P, Ingole D, Sonawane DN. Nonlinear Model Predictive Control of BLDC Motor with State Estimation. IFAC-PapersOnLine. 2021; 54:6, 107–112.
  6. Kaf AA, Cheng X, Zhang C, Almadwami A, Abdullah A, Almadwami H. Sensorless Direct Torque Control in Brushless DC Motor Using Sliding Mode Observer. 2024 4th International Conference on Emerging Smart Technologies and Applications (eSmarTA), 2024; 1–8.
  7. İnan, R, Aksoy B, Salman OKM. Estimation performance of the novel hybrid estimator based on machine learning and extended Kalman filter proposed for speed-sensorless direct torque control of brushless direct current motor. Engineering Applications of Artificial Intelligence. 2023; 126: 1-13.
  8. Tuzcu C, Mengüç EC, Demir R. İnan R, Fırçasız doğru akım motorların adaptif filtre tabanlı MRAS ile hız algılayıcısız doğrudan moment kontrolü. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi. 2025; 14: 2, 680-687.
  9. Saha B, Singh B. Torque Ripple Mitigation in Sensorless PMBLDC Motor Drive With Adaptive Observer for LEV. IEEE Transactions on Power Electronics. 2505; 40: 1, 1739–1747.
  10. [Gamazo-Real JC. Martínez-Martínez V, Gomez-Gil J. ANN-based position and speed sensorless estimation for BLDC motors. Measurement. 2022; 188:110602.
  11. Joshi D, Deb D, Giri AK. MRAS disturbance observer-based sensorless field-oriented backstepping control of BLDC motor drive. Electrical Engineering. 2024; 106: 5, 6681–6701.
  12. Demir R. Speed-sensorless Predictive Current Controlled PMSM Drive With Adaptive Filtering-based MRAS Speed Estimators. International Journal of Control, Automation and Systems. 2023; 21: 8, 2577–2586.
  13. Demir R, Yıldız R, Barut M, Speed-sensorless predictive torque control of the IM based on MRAS. Nigde Omer Halisdemir University Journal of Engineering Sciences. 2023; 12:1, 126–133.
  14. Yıldız R, Demir R, Zerdali E, Barut M. Least mean Kurtosis algorithm-based MRAS estimator for speed-sensorless model predictive control of induction motor. V. International Turkic World Congress on Science and Engineering, Bishkek, Kırgızistan. 2023; 80–92.
  15. Berberidis D, Kekatos V, Giannakis GB. Online Censoring for Large-Scale Regressions with Application to Streaming Big Data. IEEE Transactions on Signal Processing 2016; 64:15, 3854–3867.
  16. Mengüç EC, Acır N, Mandic DP. A Class of Online Censoring Based Quaternion-Valued Least Mean Square Algorithms. IEEE Signal Processing Letters. 2023; 30: 244–248.
  17. Mengüç EC, Xiang M, Mandic DP. Online censoring based complex-valued adaptive filters. Signal Processing. 2022; 200: 108638.
  18. Alzayed M, Chaoui H. Efficient Simplified Current Sensorless Dynamic Direct Voltage MTPA of Interior PMSM for Electric Vehicles Operation. IEEE Transactions on Vehicular Technology. 2022; 71:12, 12701–12710.
  19. Krasopoulos CT, Beniakar ME, Kladas AG. Velocity and Torque Limit Profile Optimization of Electric Vehicle Including Limited Overload. IEEE Transactions on Industry Applications. 2017; 53:4, 3907–3916.
  20. Mengüç EC, Mandic DP. Online censoring-based learning algorithms for fully complex-valued neural networks. Neurocomputing. 2025; 623: p129412.
  21. Romero D, Berberidis D, Giannakis GB. Quickest convergence of online algorithms via data selection. 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2016; 6185–6189.
  22. Vidhya SD, Balaji M. Modelling, design and control of a light electric vehicle with hybrid energy storage system for Indian driving cycle. Measurement and Control. 2019; 52: 1420–1433.
  23. Demir R. Elektrikli araçlar için farklı hız profilleri altında model öngörülü akım kontrollü İSMSM sürücüsünün başarımı. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi. 2024; 13:4, 1478-1484.