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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".