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

Mehmet Ünal; Rıdvan Demir; Engin Cemal Mengüç; Remzi İnan

Makale detayları

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

Yazarlar:

Mehmet Ünal | Rıdvan Demir | Engin Cemal Mengüç | Remzi İnan

Yayıncı:

Gazi University

Ana Başlıklar:

Elektrik Makineleri ve Sürücüler

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.13 Issue 3, pp.991-1001

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001353683

pISSN:

2147-9526

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 Bu çalışmada, fırçasız doğru akım motorların FDAMlerin 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 FDAMye 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 MUSun işlem süreleri geleneksel LMS temelli MUS kestirici ile karşılaştırılmış ve önerilen yöntemin işlem yükünü düşürdüğü gösterilmiştir.

Makale detayları

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

Yazarlar:

Mehmet Ünal | Rıdvan Demir | Engin Cemal Mengüç | Remzi İnan

Yayıncı:

Gazi University

Ana Başlıklar:

Elektrik Makineleri ve Sürücüler

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.13 Issue 3, pp.991-1001

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001353683

pISSN:

2147-9526

BLDC Motor

Light Electric Vehicles

Direct Torque Control

OC-LMS Algorithm

Model Reference Adaptive SystemFDAM

Hafif Elektrikli Araçlar

Doğrudan Moment Kontrol

OC-LMS Algoritması

Modele Uyarlamalı Sistem

Sayı

Makale detayları

Yazarlar: Mehmet Ünal | Rıdvan Demir | Engin Cemal Mengüç | Remzi İnan

Yayıncı: Gazi University

Ana Başlıklar: Elektrik Makineleri ve Sürücüler

Dergi: Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.13 Issue 3, pp.991-1001

Belge Tipi: Makale

Yayın Tarihi: 9/2025

Belge Dili: Türkçe

Document ID (Org No.): 2001353683

pISSN: 2147-9526

Sayı

Yazarlar:

Mehmet Ünal | Rıdvan Demir | Engin Cemal Mengüç | Remzi İnan

Yayıncı:

Gazi University

Ana Başlıklar:

Elektrik Makineleri ve Sürücüler

Dergi:

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji Vol.13 Issue 3, pp.991-1001

Belge Tipi:

Makale

Yayın Tarihi:

9/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001353683

pISSN:

2147-9526