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

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.

Keywords

• BLDC Motor
• Light Electric Vehicles
• Direct Torque Control
• OC-LMS Algorithm
• Model Reference Adaptive System

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

Öz

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.

Anahtar Kelimeler

• FDAM
• Hafif Elektrikli Araçlar
• Doğrudan Moment Kontrol
• OC-LMS Algoritması
• Modele Uyarlamalı Sistem

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