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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".