Design and Implementation of a Four-Quadrant DC-DC Converter Based Adaptive Fuzzy Control for Electric Vehicle Application
Bibliographic record
Abstract
Electric vehicles (EVs) provide an excellent opportunity for limiting the emission of a variety of environmentally hazardous gases caused by gasoline and diesel-based vehicles. These propelled vehicles require a forward and backward motion as well as a variable speed operation. Hence, the use of a four-quadrant (4-Q) direct current (DC) converter becomes a necessity. This paper aims to analyse the traction system of an electric automobile along with the improvement of energy efficiency. Inserting a bi-directional DC-DC converter between the battery and the four quadrant-DC chopper assembly allows the power flow from the battery to the motor and the other way around during regenerative braking. Therefore, increasing the limited driving range of the EV. This paper also focused on the application of model reference adaptive fuzzy control (MRAFC) in order to adjust the direct current bus voltage and the DC motor speed. The proposed system has been tested on an experimental bench and the results have been analysed.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".