A High-Efficiency High-Power-Density On-Board Low-Voltage DC–DC Converter for Electric Vehicles Application
Bibliographic record
Abstract
The on-board low-voltage dc-dc converter (LDC) in electric vehicles (EVs) is used to connect the high-voltage battery with the low-voltage auxiliary system. With the advancement of auxiliary equipment in EVs, the output current of the LDC can be hundreds of amperes, which will cause high-conduction loss and severe thermal concern. In this article, a high-efficiency high-power-density on-board LDC is presented. To reduce current stress and improve efficiency, three-phase interleaved LLC dc-dc converters are paralleled to provide 270 A load current. Synchronous rectifier is used to reduce secondary conduction loss. zero-voltage-switching (ZVS) turn-on of primary switches and ZCS turn-off of secondary switches are achieved, thus switching loss can be reduced significantly. Moreover, phase-shedding technology is used to improve light load efficiency. Switch-controlled capacitor (SCC) technology is used to achieve accurate load current sharing among the three phases, which protects the devices against high-current stress, reduces the conduction loss, and improves the reliability of the system. As SCC switches achieve ZVS turn-on and turn-off by its nature, the loss of the SCC circuit is of less concern with regard to the rated output power. In addition, GaN HEMTs are used in the primary side to improve the power density and eventually help achieving light weight. A 3.8-kW (14 V/270 A) LDC prototype is developed and tested. Experimental results show good current balancing among the three phases. A peak efficiency of 96.7% at 140 A load and a full load efficiency of 95.8% are achieved with 3 kW/L power density and 1.5 kg weight.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".