Multi-Mode PWM Modulator for a 4-Switch DC-DC Converter Operating with Tri-State
Bibliographic record
Abstract
The 4-switch bi-directional (Buck+Boost) dc-dc converter is a good choice for interfacing DC buses where the voltage of one bus can be higher or lower than the other. It can operate with four different states determined by a pair of ON switches. The simplest and most common modulation schemes employ only two of them, in a dual-state logic. Tri-state operation can eliminate the right-half plane zero from the transfer function output-to-control variable in Boost-derived modes of operation. It can also reduce the inductor current ripple and rms value. A similar effect can be obtained by changing the order of the states used in a switching period as the power flow reverses. The issue of switching losses can be addressed with zero voltage switching (ZVS) but requires the inductor current to reverse in every switching cycle. Operation with ZVS frequently employs all four states. This paper describes a new carrier-less PWM modulator that allows the change of the mode of operation and sequence of states in a simple way. Its performance is verified by simulation of the 4-switch converter in a typical application: As the interface of a supercapacitor connected to a DC bus employing a tri-state scheme with possible reversal of the states of operation.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".