Multi-Variable Optimization Framework for Converters: Enhancing Single-Point and CEC-Weighted Efficiency
Bibliographic record
Abstract
This paper presents a comprehensive optimization framework for enhancing the efficiency of a power converter through the simultaneous tuning of key design variables, including dead time, snubber capacitance, gate resistances, and gate drive voltages. Detailed loss modeling is performed for Silicon (Si) and Gallium-Nitride (GaN) devices, incorporating hard and soft switching behaviors, reverse conduction losses, gate drive losses, and core losses. Unlike conventional approaches that only focus on conduction and/or switching loss minimization, the proposed method targets single-point efficiency as the optimization objective function under varying load conditions. The optimization ensures thermal constraints are satisfied by integrating iterative thermal modeling within the loss calculation loop. Simulation and experimental results validate the proposed methodology across multiple power levels, including Single point 1 kW, 4 kW, and California Energy Commission (CEC)-weighted efficiency targets. The results demonstrate that dynamic dead time adjustment and coordinated snubber-gate optimization significantly improve efficiency and reduce switching losses. The optimized converter achieved a peak efficiency of 98.88% at 4 kW and maintained high performance across the full load range, confirming its applicability for high-frequency power conversion in electric vehicle chargers and energy storage systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".