An Auxiliary-Assisted Dual-Inductor Hybrid DC-DC Converter with Adaptive Inductor Slew Rate for Fast Transient Response in 48-V Automotive PoL Applications
Bibliographic record
Abstract
This paper presents an auxiliary-assisted control scheme for fast transient response in 48V-to-1V automotive Point-of-Load (PoL) applications. The proposed scheme performs dc power delivery and output voltage regulation using a main and auxiliary stage, respectively. A 4:1 Dual-Inductor Hybrid (DIH) dc-dc converter delivers dc load power by regulating the auxiliary capacitor voltage,$V_{AUX}$, which enables adaptive control of the auxiliary inductor current slew rate for fast transient response. An auxiliary AC-coupled Buck (ACB) converter regulates the output voltage based on an output-capacitor current-based Hys-teretic Current-Mode-Control (HCMC) scheme. An Adaptive-Voltage-Positioning (AVP) control scheme is proposed for$V_{AUX}$, which prepositions the auxiliary-inductor slew rate for improved transient response. A simulation model was built to validate the proposed control scheme. Simulation results demonstrate that the proposed control scheme including AVP decreases the auxiliary capacitance by 40%, the auxiliary output rms current by 28%, and the main-stage peak current by 30 A. The system achieves a peak simulated efficiency of 92.6% with an output capacitance of only 640$\mu \mathrm{F}$and an auxiliary capacitance of 30$\mu \mathrm{F}$.
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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 | 0.000 |
| 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".