A novel approach towards electrical loss minimization in vector controlled induction machine drive for EV/HEV
Bibliographic record
Abstract
The usage of niche copper-rotor induction motor (CRIM) in all the variants of the Tesla Roadster electric vehicle has bolstered the technology of using induction motor for electrified transportation. Understanding the merits, demerits and state of art technology of induction motor and its drive in electric and hybrid electric vehicle (EV/HEV) application, this research manuscript proposes a novel approach towards electrical loss minimization in vector controlled induction machine drives for the aforementioned application. This paper serves as a good theoretical study of the developed loss minimization scheme which would be later incorporated into an overall vector controlled induction machine drive for enhancing the efficiency of the electrified vehicle. Firstly, an insight is provided on the state or art induction motor technology in EV/HEV and the significance of incorporating core loss in an induction machine. Secondly, the conventional two-axis model of an induction motor incorporating core loss has been used to propose a novel loss minimization algorithm. Rotor flux has been used as the control variable for this purpose.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".