Fault Detection in a Hybrid Dickson DC–DC Converter for 48-V Automotive Applications
Bibliographic record
Abstract
Autonomous driving features have introduced safety-critical processor modules. A 48-V network has been developed to enable efficient power distribution. A fault-tolerant high-voltage power management unit is required to ensure continuous power delivery to critical loads. This article compares the cost and efficiency penalty of accommodating the fault-tolerant capability in common dc-dc topologies. The comparison is verified using a 0.13-μm high-voltage automotive Bipolar-CMOS-DMOS SMARTMOS technology in a Cadence simulation environment. Besides, a quantitative reliability assessment of the single-phase and multiphase configurations is presented. This has led to the selection of the hybrid Dickson topology, which has the best electrical performance and comparable cost and reliability among all for high-conversion-ratio fault-tolerant 48-V automotive application. Moreover, this article presents a fast and robust short-circuit and open-circuit fault detection scheme for power switches and flying capacitors in a hybrid Dickson dc-dc converter. The detection method only observes the low-voltage switching node, which eliminates the challenges associated with high-voltage high-bandwidth sensing. The performance of the design has been verified using a multiphase 48-V-to-3.3-V 4-to-1 Dickson converter prototype. The measured results demonstrate that the short-circuit faults are detected within two switching cycles of 250 kHz, which is less than the 10-μs short-circuit immunity of commercial silicon devices.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".