Generalized State Space Average Model for Multi-Phase Interleaved Buck, Boost and Buck-Boost DC-DC Converters: Transient, Steady-State and Switching Dynamics
Bibliographic record
Abstract
This paper presents a generalized state space average model (GSSAM) for multi-phase interleaved buck, boost and buck-boost converters. The GSSAM can model the switching behavior of the current and voltage waveforms, unlike the conventional average model which can model only the average value. The GSSAM is used for the converters with dominant oscillatory behavior such as resonant converters, high current ripple converters, and multi-converter systems. The maximum current and voltage through the system can be predicted by modeling the switching behavior of voltage and current. The GSSAM in the literature is introduced for single-phase converters only, and it is not introduced for multi-phase converters due to the high complexity associated with it. Hence, the GSSAM for multi-phase buck, boost and buck-boost converters are introduced in this paper and the proposed models can fit with converters of any number of phases. The number of operating phases in the multi-phase interleaved converters is proportional with the output power to achieve the maximum efficiency over the operating range. Therefore, the proposed GSSAMs can describe the operation at any number of operating phases with switching dynamics of phases. The proposed GSSAM is validated by comparing the transient and steady-state dynamics between the GSSAM and a switching model from PLECS.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".