Efficiency-Oriented Optimized Design and Control of Hybrid FSBB–<i>CLLC</i> Converters With Partial Power Processing Capability
Bibliographic record
Abstract
Combining the highly efficient CLLC topology with the exceedingly flexible Four-Switch Buck-Boost (FSBB) topology, this paper introduces a novel hybrid FSBB-CLLC converter incorporating partial power processing (PPP) capability. This hybrid structure utilizes FSBBs to regulate its output voltage by adjusting its duty cycle, handling a small portion of the total power, and providing a fast dynamic response. Meanwhile, the CLLC in the structure operates in a complete resonant state to ensure high system efficiency. By sharing a bridge arm between the FSBB and the CLLC, where one arm from each system serves a similar function, the number of switches utilized is significantly reduced, resulting in higher system efficiency. In addition to the structural improvements, to achieve a higher average system efficiency under different loads, this paper presents an efficiencybased parameter design methodology. Furthermore, phase shift, an additional control freedom of FSBB, is employed to further enhance the system's overall efficiency; however, calculating the relationship between efficiency and phase shift can be burdensome, especially in real-time controllers. To address this issue, this paper proposes a fitting-model-based maximum efficiency tracking (FMET) approach to reduce calculation complexity. Ultimately, experimental results demonstrate the effectiveness of the proposed design, highlighting its enhanced performance.
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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".