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Record W4409058082 · doi:10.1109/tia.2025.3556658

Torque Ripple Reduction in Brushless DC Motors with 180° Commutation

2025· article· en· W4409058082 on OpenAlexaff
Ziliang Feng, Rahul Raman Ramesh, Ekamjot Singh Tahim, Jiahao Zhang, Seyyedmilad Ebrahimi, Juri Jatskevich

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Motor Design and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCommutationDC motorBrushed DC electric motorTorque rippleControl theory (sociology)TorqueRippleDirect torque controlTorque motorReduction (mathematics)Stall torqueComputer scienceElectrical engineeringAutomotive engineeringAC motorEngineeringElectric motorInduction motorPhysicsVoltageMathematicsControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

Hall-sensor-based brushless DC (BLDC) motors are utilized extensively in low-cost applications due to their manufacturing simplicity and ease of control. Often, BLDC motors are controlled using the 120° commutation logic, which naturally approximates the maximum torque per ampere (MTPA) operation. However, in some applications, the 180° commutation is preferred since it allows higher DC voltage utilization and continuous phase currents, although it comes at the cost of higher torque ripple. This paper proposes a new torque control strategy to reduce the torque ripple in the 180°-commutated BLDC motors. The proposed method dynamically adjusts the duty cycle within each switching interval based on the reference, the estimated electromagnetic torque, and the derivative of the torque. The proposed method is analyzed under different speeds and back EMF shapes, and its effectiveness is validated through simulation and experimental results on a typical industrial BLDC motor.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.945
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.229
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

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