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Journals / Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi / 2026 / Cilt: 17

Extended Kalman filter based speed-sensorless model predictive current control of BLDC motor drives used in e-scooters

E-Skuterlerde kullanılan FDAM sürücülerinin genişletilmiş Kalman filtresi tabanlı hız-algılayıcısız model öngörülü akım kontrolü

Rıdvan Seyit Avcuoğlu(Kayseri University)0009-0000-9311-4565
Rıdvan Demir(Kayseri Üniversitesi Mühendislik, Mimarlık ve Tasarım Fakültesi, Elektrik Elektronik Mühendisliği Bölümü)0000-0001-6509-9169
Pages
—
Publication Date
2026-07-03
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AI Summary & Key Takeaways

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

Simulation results obtained over a wide speed range, including zero speed, and under varying load conditions demonstrate both the high estimation accuracy of the EKF observer and the effective control performance of the MPCC-based BLDC motor drive.

📌 3 Key Takeaways

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Objective & Hypothesis:This study presents the design and simulation-based validation of an extended Kalman filter (EKF) observer for the speed-sensorless model predictive current control (MPCC) of brushless direct current (BLDC) motors used in electric scooters (e-scooters).
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Key Findings & Data:To evaluate the system under realistic operating conditions, the estimation and control performances are validated using standard driving profiles.
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Conclusion & Impact:Simulation results obtained over a wide speed range, including zero speed, and under varying load conditions demonstrate both the high estimation accuracy of the EKF observer and the effective control performance of the MPCC-based BLDC motor drive.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Fırçasız doğru akım motoru · Genişletilmiş Kalman filtresi · Model öngörülü akım kontrolü

Abstract

This study presents the design and simulation-based validation of an extended Kalman filter (EKF) observer for the speed-sensorless model predictive current control (MPCC) of brushless direct current (BLDC) motors used in electric scooters (e-scooters). The designed EKF simultaneously estimates the stator currents, rotor speed, electrical rotor position, and load torque of the BLDC motor. To evaluate the system under realistic operating conditions, the estimation and control performances are validated using standard driving profiles. In addition, the external load torque applied to the motor model during simulations is dynamically derived from these driving profiles and the traction force of the e-scooter. Simulation results obtained over a wide speed range, including zero speed, and under varying load conditions demonstrate both the high estimation accuracy of the EKF observer and the effective control performance of the MPCC-based BLDC motor drive.

Özet

Bu çalışmada, elektrikli skuterlerde (e-skuter) kullanılan fırçasız doğru akım motorlarının (FDAM) hız-algılayıcısız model öngörülü akım kontrolü (MÖAK) için genişletilmiş Kalman filtresi (GKF) tabanlı bir gözlemleyici tasarlanmış ve benzetim ortamında test edilmiştir. Tasarlanan GKF gözlemleyicisi, FDAM’nin stator akımlarını, rotor hızını, elektriksel rotor konumunu ve yük momentini eşzamanlı olarak kestirmektedir. Sistemi gerçekçi çalışma koşulları altında test etmek amacıyla, kestirim ve kontrol başarımları standart sürüş profilleri kullanılarak doğrulanmıştır. Ek olarak, benzetim çalışmalarında FDAM modeline uygulanan yük momenti, sürüş profilleri ve e-skuter’e ait toplam çekiş kuvveti kullanılarak belirlenmiştir. Sıfır hız dahil geniş bir hız aralığında ve farklı yük koşulları altında gerçekleştirilen benzetim sonuçları, hem GKF gözlemleyicisinin yüksek kestirim doğruluğunu hem de MÖAK tabanlı FDAM sürücüsünün etkin kontrol başarımını ortaya koymaktadır.

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Cite This Article

Avcuoğlu, R. S., Demir, R. (2026). Extended Kalman filter based speed-sensorless model predictive current control of BLDC motor drives used in e-scooters. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, Cilt: 17. https://doi.org/10.28948/ngumuh.1949569

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