High Efficiency Bidirectional LLC+C Resonant Converter With Parallel Transformers for Solar-Charged Electric Vehicles
Bibliographic record
Abstract
Recent interest in electric vehicles with onboard solar generation presents a new opportunity for using solar electric vehicles (SEVs) for distributed generation when the traction battery is fully charged. This concept presents a new power electronic challenge: the onboard charger should have high efficiency in the grid-to-vehicle (G2V) direction for normal charging power levels (i.e., 6.6 kW) and also high efficiency in the vehicle-to-grid (V2G) direction for much lower solar power levels (i.e., < 800 W). However, typical onboard charger topologies have low efficiency at light loads. This article proposes a novel bidirectional LLC+C converter to address this challenge. It operates as a full-bridge LLC converter in the G2V direction and a half-bridge LLCC converter with a smaller transformer in the V2G direction. Simulation results show a maximum efficiency gain of 5.1% in the V2G direction compared to the standard full-bridge LLC converter. To experimentally validate the topology and operation, a 6.6-kW prototype with 450–700-V input is designed, built, and tested. The experimental results show that the proposed converter achieves high efficiency in the V2G direction at 700 W (95.7%).
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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) | 0.003 | 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".