Transformer Design for Solid-State Transformer (SST)-Based EV Charging Station Applications
Bibliographic record
Abstract
Solid-state transformers (SSTs) are extensively studied nowadays to replace the line-frequency transformers (LFT) in various applications such as EV charging stations. The SST technology provides more controllability over the system, has the ability of power factor correction, and drastically reduces the size and weight of the whole system thanks to the medium-frequency transformer (MFT) used instead of LFT. In this paper, an optimized design tool is developed to design a 100 kW, 20 kHz transformer to be used in an SST-based EV charging station. The tool takes the system specifications as an input from the user then chooses the optimal core, number of turns, and Litz wire, to accelerate the design process. Two scaled-down 5 kW MFTs are implemented. The first was designed using the commonly used method in literature which ensures no saturation occurs in the core and limits the core losses to a certain value. Then, the transformer designed based on the developed tool is compared with the conventional design in terms of volume and efficiency. The transformer is also implemented and tested to compare the leakage inductance with the analytical and simulated values.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".