Ultrafast Transient Response in 48 V Automotive VRMs: An Auxiliary-Assisted Adaptive Slew-Rate Control Scheme
Bibliographic record
Abstract
This article presents an auxiliary-assisted hybrid dc–dc converter for 48 V automotive voltage regulator modules (VRMs) that enable adaptive inductor-current slew-rate control for ultrafast transient response. The main stage, a 4:1 dual-inductor hybrid (DIH) dc–dc converter, delivers the dc load power, while the output voltage is regulated by a GaN-based auxiliary-buck stage that is ac-coupled by a buffer capacitor. The main stage regulates the output of the ac-coupled auxiliary stage using an average-current-mode-control (ACMC) scheme to achieve adaptive control of the auxiliary-inductor-current slew rate. The auxiliary ac-coupled buck (ACB) converter regulates the output voltage based on an output-capacitor current-based hysteretic-current-mode-control (HCMC) scheme. An adaptive-voltage-positioning (AVP) scheme is proposed for the auxiliary capacitor, which preemptively positions the ACB output voltage for improved transient response. A small-signal model of the auxiliary-assisted converter is presented for stability analysis and validated with simulation results. A 40 W proof-of-concept prototype was fabricated to demonstrate the feasibility of the adaptive slew-rate control technique and AVP scheme. The prototype achieves a peak efficiency of 90.6% with an output capacitance of 500$\boldsymbol{\mathbf{\mu}}$F and an auxiliary capacitance of 22$\boldsymbol{\mathbf{\mu}}$F, while maintaining the output voltage deviation within 50 mV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
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".