A Fully Soft-Switched Resonant Based DC-DC Converter using Adder Architecture for Fast EV Battery Charging Applications
Bibliographic record
Abstract
Persistent efforts are being made to enable electrification of the transport industry for a cleaner environment. Though there has been substantial increase in the number of electric vehicles (EVs); lack of adequate and quick charging points are proving to be major stumbling blocks to the wide spread adoption of same. This paper presents a potential DC-DC converter architecture intended for fast battery charging applications. The converter is capable of generating wide output voltage (150V-920V) while maintaining soft-switching transitions for all the switches in the mentioned voltage range. It is conceived on the principle of adder architecture in which multiple power conversion modules are connected in a series-parallel combination for processing high power (55-175 kW). Detailed modelling and analysis of the converter is presented to select system components. A scaled down experimental prototype (voltage range of 36V-218V and power range of 0.32kW – 1.2kW) showing 97% peak efficiency is built to demonstrate proof of concept.
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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 | 0.000 | 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".