Comparative analysis of DITC and DTFC of switched reluctance motor for EV applications
Bibliographic record
Abstract
Switched Reluctance Motor (SRM) presents simple construction, high starting torque, wide speed range, high efficiency and high reliability which are appropriate for an Electric Vehicle (EV) drive system. However, SRM has some issues such as high torque ripple, acoustic noise, vibration due to non-linearities, and require a position sensor for control. The purpose of this paper is to compare two different control techniques and to find which method can provide better performance in terms of controllability and efficiency. The first technique is Direct Instantaneous Torque Control (DITC), i.e only torque is controlled with in the hysteresis band. The second technique is Direct Torque and Flux Control (DTFC), i.e both torque as well as stator flux vector is controlled in the hysteresis band. Principles of both these methods are discussed in detail. The performance analysis of these methods for 6/4 SRM are carried out and validated from simulation results. Conclusions are drawn from the results and the advantages and drawbacks of each method are addressed.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".