Modeling the Bi-directional DC-DC Converter for HEV's
Bibliographic record
Abstract
Hybrid Electrical Vehicles-HEV!?s are the importnt ways to improve vehicle performance. The transformer isolated bi-directional DC-DC converters are the key components of the traction system in HEV!?s. This paper presents adetail mathematic model of isolated bi-directional DC-DC converter for HEV!?s. Approximate models are important mathematic methods especially for analysis and closed-loop control design converter circuits. These differential equations, which govern the converter operation, change periodically among a set of linear differential equations because of the switch effect. Basing on the time-scale the state variables was separate as fast-scale and slow-scale variables. The fast changing variable of the leakage inductor was eliminated by substitute the fast-scale variable into slow-scale variable equations, resulting in reduced order differential equations. From this set of reduced orderdifferential equations the completely averaged model of the isolated DC/DC converter was derived. The simulated results reveal that the circuit and mathematical model are consistent very well. The averaged state variables can be treated as a small component plus a DC component, so the averaged model can be separated a dynamic small signal part and a DC part. This linearized small signal model is suit for control design and analysis at a steady point that is decided by the DC component. As an example a PI controller was design basing on the linear model.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".