Switch Current-Based Closed-Loop Control for ZCS Bidirectional Current-Fed DC-DC Converters
Bibliographic record
Abstract
A significant challenge of current-fed DC-DC converters is managing the primary-side switch voltage stress at turn-off. One way to manage this stress is to use advanced modulation techniques that ensure soft turn-off of the primary-side switches. These advanced modulation techniques work by predicting the switch currents at turn-off and, therefore, are partially open loop. Consequently, due to circuit tolerances and converter non-idealities, significant control margin must be added in practical settings to ensure soft-switching is achieved over the full operating range of the converter. This control margin leads to increased switch current at turn-off and, therefore, increased circulating currents within the converter, impacting the overall efficiency potential. In this paper, a novel switch current-based closed-loop control technique is proposed that incorporates actual switch current measurements within the modulation technique, enabling the minimization of the switch currents at turn-off and, therefore, the circulating currents. The paper presents the operation, implementation, simulation results, and hardware-in-the-loop results obtained using an RTDS NovaCor system and B-Box RCP from Imperix.
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 | 0.000 | 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) | 0.002 | 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".