High efficiency LLC converter design for universal battery chargers
Bibliographic record
Abstract
In order to support different types of rechargeable batteries (e.g. Li-Ion, Lead-Acid, NiMh), the design of universal battery chargers must focus on wide conversion efficiency instead of traditional peak efficiency design. Wide efficiency is the ability to maintain high performance within the nominal output power while supporting the charging cycle voltage of different battery technologies. The objective of this paper is to tackle this new wide efficiency technical challenge and provide a design methodology that focuses on multiple operating points rather than obtaining peak efficiency at one operating point. The universal battery charger is expected to provide a demanding output voltage range between nominal and 1.5 times nominal and sustaining maximum power delivery with high efficiency. The proposed LLC converter design procedure successfully selects the resonant tank elements and operating frequencies to maximize efficiency for the maximum power region. The design procedure employs analytical equations and a Tabu Search algorithm (TS) for a 96V DC, 960W universal battery charger implementation. The experimental results exhibit the excellent performance of the designed converter, which has an average efficiency of 96.1% within the nominal output power delivery range (between 96V DC and 144V DC output voltage range) with extreme regulation capability.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".