A High Efficiency High Power-Density LLC DC-DC Converter for Electric Vehicles (EVs) On-Board Low Voltage DC-DC Converter (LDC) Application
Bibliographic record
Abstract
This paper presents a high efficiency high power-density LLC DC-DC converter for Electric Vehicles (EVs) on-board low voltage DC-DC converter (LDC) application. In the proposed LDC, primary switches achieve ZVS turn-on and secondary synchronous rectifier switches achieve ZVS turn-on and ZCS turn-off. To reduce current stress and improve efficiency, three phase interleaved LLC DC-DC converters are paralleled to provide more than 200A load current. Switch-Controlled Capacitor (SCC) technology is used to achieve the load current sharing of the three phase LLC DC-DC converter. In addition, GaN HEMTs are used in the transformer primary to improve the switching frequency and power-density. To verify the analysis, a 3.8kW(14V/270A) LLC DC-DC converter prototype is designed. The experimental results show that full load efficiency is 95.8% at 270A load current and 3kW/L power-density is achieved.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".