Optimized Design and Fast-Dynamic Control for ISOP-Connected Hybrid <i>CLLC</i>-DAB System With Partial Power Processing Property
Bibliographic record
Abstract
This paper presents new design and control approaches for an input-series-output-parallel (ISOP) connected hybrid converter with partial power processing (PPP) capabilities, which comprises a CLLC converter and a dual-active-bridge (DAB). In this hybrid CLLC-DAB configuration, secondaryside bridges are shared between CLLC and DAB topologies to minimize the number of utilized switches. Incorporating the high efficiency of CLLC with the exceptional control flexibility of DAB, the hybrid configuration transfers the main power through CLLC while enabling DAB to handle partial power flow. The power distribution between CLLC and DAB is optimized, considering system efficiency, dynamic response, etc. Based on the resulting power ratio, system parameters are also optimized, including considerations for soft-switching of all switches in a wide load range, output power capacity, component tolerances, and overall efficiency. Furthermore, the output voltage can be accurately regulated by manipulating the phase shift angle of DAB. To enhance the system's transient performance in scenarios involving varying loads and input voltages, an adaptive fast dynamic response control (AFDRC) strategy exhibiting robustness against variations in system parameters is also proposed. Finally, experimental results validate that the hybrid CLLCDAB system, employing the proposed design methodology and control strategy, attains both high system efficiency and ultrafast dynamic response.
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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".