Design of Fuzzy-ACO Based Controller for Cuk Converter in Electric Vehicles
Bibliographic record
Abstract
The voltage output of an energy storage device utilized in electric vehicles (EVs) is modulated in response to the load, and the elevated voltage of the DC link poses challenges for vehicle engineers when integrating energy storage components with the traction drive system.DC-DC converters are commonly employed in electrical powertrain systems within industrial settings to facilitate the integration of various components.The Cuk converter is widely acknowledged as a viable alternative requiring thorough deliberation, including battery management systems (BMS) for EVs technology.This is because of a continuous flow of current in both the input and the output, and it has a high efficiency compared to the buck-boost topology.Additionally, there is a low ripple in the output load voltage.The present work uses an analysis, model, and control of the Cuk converter circuit using a hybrid fuzzy logic-based ant colony optimization (ACO) method.The transient response and the steady-state performance specifications are objective functions in the fuzzy controller design process.After a comprehensive evaluation of the obtained results, it was observed that the fuzzy-ACO controller successfully achieved the desired reference with reduced rise and settling time.This study implements modeling and controlling applications of the Cuk converter by utilizing the MATLAB/SIMULINK program.The proposed controller's effectiveness is demonstrated during load variations and changes in the reference voltage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".