Asymmetric Parameters Design for Bidirectional Resonant CLLC Battery Charger
Bibliographic record
Abstract
The CLLC bidirectional resonant converter has significant potential in chargers and DC microgrids, due to its bidirectional power transfer capability. To ensure characteristics consistency of bidirectional operation, traditional symmetrically designed resonant tanks are usually adopted. Traditional symmetrical tanks are effective for the application without voltage regulation such as CLLC DC transformers. However, for bidirectional battery chargers, the bidirectional operating characteristics are not the same, because of asymmetric voltage and load of two sides. Therefore, the traditional symmetrical resonant tank design would cause undesirable large frequency range to provide wide voltage gain range, especially for Wide Battery Voltage Range (WBVR) operation. To address conventional symmetric design issues, detailed asymmetric parameters methodology(APM) is proposed in this paper. Reasonable ranges of each normalized parameter are obtained by considering the constraints of APM. Based on these ranges, the statistical Design of Experiment (DoE), instead of using FHA model, is adopted to obtain more precise frequency responses of CLLC converter towards variations of parameters in wide switching frequency range. The APM enables more similar switching frequency ranges in Charge Mode (CM) and Discharge Mode (DM), thereby reducing the overall frequency range of WBVR operation. This could lower switching loss caused by excessive high-frequency charging under Low Battery Voltage (LBV) in CM, and relieve the extra conduction loss and current stress of power components as well when batteries is discharged at LBV in DM. Finally, the proposed method is proved by experiments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".