High-Efficiency Dual-Phase LLC Converter With Asymmetric Resonant Tanks and Switch-Controlled Capacitor for EV Auxiliary Power Modules
Bibliographic record
Abstract
A novel asymmetrical resonant tank design is proposed for dual-phase LLC DC-DC converters used in the auxiliary power module (APM) of electric vehicles (EVs), featuring built-in redundancy. The proposed design ensures that the impedance of one phase remains consistently either higher or lower than the other phase’s impedance across a wide input and output voltage range. Consequently, a single Switch-Controlled Capacitor (SCC) circuit suffices for effective active current sharing, reducing system complexity and implementation costs without compromising efficiency or performance. Each phase of the proposed converter is designed separately to meet the requirements of a wide voltage gain range, while maintaining an expected voltage gain relationship between the phases. A sensitivity analysis was conducted, considering the maximum phase-to-phase mismatch resulting from ±5% component tolerances between the two phases. Experimental results from a full-scale APM implementing the proposed dual-phase LLC DC-DC converter, operating with an input voltage of 250 V to 475 V, an output voltage of 9 V to 16 V, and a maximum output current of 285 A (4 kW output power), demonstrate the design’s success in achieving effective current sharing across input/output voltage and load ranges. Furthermore, the implemented APM achieves a peak efficiency of 96.3% and a load average efficiency exceeding 95.6% across the HV battery voltage range.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".