Converter Topology Comparison for a Two-Stage Level-2 Onboard Charger in 800-V EV Powertrains
Bibliographic record
Abstract
The operating voltage of powertrains in Battery Electric Vehicles (BEVs) has witnessed an upward trend due to advantages in ultra-fast charging and reduction in run-time losses due to lower operating currents in the powertrain. Original Equipment Manufacturers (OEMs) such as Porsche, Audi, and Lucid Motors have introduced vehicles with powertrains operating from 800 V to 924 V. Due to increased switching losses at these voltage levels, the operation of DC-DC converters requires cascading of existing topologies or a multi-level operation. With increasing battery sizes, traditional Level-2 3.3 kW on-board chargers (OBCs) are deemed insufficient to fully complete a charge overnight. Thus, due to increasing voltage and power levels in commercial BEVs, this paper investigates the Grid to Vehicle (G2V) mode’s priority operating regions for a 11.5 kW Level-2 on-board charger in context of an 800 V powertrain. A design procedure for three potential DC-DC converter configurations is performed, and an optimization process is established. A multi-domain comparison highlighting the cost, advantages, and disadvantages of each configuration has been presented.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.007 | 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".