4-DoF Control and Efficiency Oriented Co-Optimization of a 15-kW Multilevel Series Resonant DAB (ML-SRDAB) Converter
Bibliographic record
Abstract
This paper proposes a co-optimization framework of key design parameters and four-degree-of-freedom (4-DoF) control for a 15-kW Multilevel Series Resonant Dual Active Bridge (ML-SRDAB) converter. The proposed DC/DC converter is suitable to meet the requirements of an SAE J3068-compliant on-board charger (OBC) in an electric vehicle (EV) with a 1.25 kV powertrain. A generalized harmonic approximation (GHA) based model that estimates the high-frequency link voltages and current in a 4-DoF controlled ML-SRDAB is developed. A framework to design the resonant tank component set for variation in turns-ratio, based on the first-harmonic approximation (FHA), is proposed. The resonant tank selection framework and steady-state model under 4-DoF control are used to arrive to an optimal value of the inductor, capacitors, and turns ratio for maximizing the time weighted average efficiency (TWAE) over the DC/DC converter's operating range. The co-optimization achieves a 98.05% TWAE for the proposed OBC.
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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".