Enhanced Direct Torque Control of SRM Based on a Novel Multilevel Hysteresis Torque Band With Effective Voltage Vectors for Low Torque Ripple
Bibliographic record
Abstract
Due to their robust rotor structure and fault-tolerance characteristics, switching reluctance motors (SRMs) are the choice of next-generation electric vehicle (EVs) traction motor applications. However, this SRM drive suffers from high torque ripples, which may cause severe vibration and acoustics. The existing SRM-operated direct torque control (DTC) using 8 Voltage vectors (VVs) gives high torque ripples due to the minimum selection of switching states and improper sector partition. On the other hand, a two-level hysteresis torque band can result in torque ripples. Therefore, existing DTC for selecting VVs produces high torque ripples in SRM. This paper proposed DTC using active small and large VVs and a multilevel hysteresis torque band (MHTB) strategy to mitigate the torque ripple further. The selection of VVs and sectors is organized in the optimal values for the three and four phases. More active VVs (i.e., 16) are employed in the modified sector-based switching tables, suppressing the torque ripples. The proposed strategy is verified and validated using MATLAB/Simulink. The detailed result discusses the response of torque, flux, and speed of SRM. The DTC-operated SRM drive experimental results are shown to prove the effective minimization of torque ripples in the proposed DTC compared to the existing DTC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".