High Efficiency LLC Resonant Converter With Wide Output Range of 200–1000 V for DC-Connected EVs Ultra-Fast Charging Stations
Bibliographic record
Abstract
DC-connected ultra-fast chargers consist of isolated DC/DC converter modules to provide the required charging profile. This paper proposes a charging module that interfaces with a fixed voltage DC-bus and covers a wide variety of electric vehicles (EVs) with battery voltages ranging from 200 to 1000 V. DC/DC converters for EVs charging has been discussed several times in the literature, however the DC-connected structure with fixed DC-bus voltage is rarely considered. This paper proposes a full-bridge LLC DC/DC converter module with a configurable secondary. This configuration extends the converter’s charging voltage range without adding stress on the resonant components or operating far from the unity gain point. Due to wide output range, the converter parameters are optimized using the time-domain analytical model to cover the required range without adding unnecessary circulating current that compromises the efficiency. First, the LLC resonant converter fundamentals are briefly introduced. Then, the proposed topology operation and advantages are discussed. After which, the converter’s requirements and design method are presented, focusing on circulating current minimization. Finally, the effectiveness of the proposed converter and the optimized design strategy is verified experimentally on a 10 kW prototype with an input of 800 V and an output of 200 - 1000 V.
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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) | 0.004 | 0.002 |
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".