Enhancing the Efficiency of Dual Active Bridge Converter in Low-Voltage Supercapacitor Applications
Bibliographic record
Abstract
This paper investigates discontinuous conduction mode (DCM) in the Dual Active Bridge (DAB) DC-DC converter to enhance its operational efficiency in supercapacitor (SC)based energy storage systems (ESS). SCs' low-voltage and high current characteristics pose a challenge for DAB when the converter needs to operate under a high-voltage conversion ratio. At high-voltage conversion ratio, conventional switching mode such as the continuous conduction mode (CCM) leads to higher conduction and switching losses. To address these challenges in efficiency, this study employs DCM switching as an alternative to CCM, which introduces a zero-crossing time (ZCT) interval during switching transitions. DCM facilitates zero-current switching (ZCS) and reduces conduction losses. Additionally, the elimination of specific switching transitions under low-voltage operating conditions enables consistent ZCS, further minimizing switching losses. This approach is particularly advantageous in configurations involving a highvoltage primary and a low-voltage, high-current secondary. Analytical modeling and steady-state derivation are provided to characterize the current and power behavior under different switching modes, and the theoretical findings are validated through simulation. The results demonstrate that DCM operation significantly lowers the power losses at low SC voltages, confirming the effectiveness of the ZCT-to-ZCS transition in enhancing DAB converter efficiency for SC applications.
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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 | 0.001 |
| 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".