A Multi-Mode Full-Bridge/Modified-Stacked- Switches Structured CLLC Resonant Converter for Energy Storage Applications
Bibliographic record
Abstract
In this article, a new full-bridge/modified-stackedswitches multi-mode CLLC isolated resonant converter is presented for energy storage applications. In particular, a multimode wide voltage gain control system is proposed, which takes advantage of the topology reconfiguration method in both the inverter and the rectifier blocks in the proposed circuit to regulate the output voltage while preventing the switching frequency from drifting away far from the resonant frequency. The impact of the parasitic elements in the isolated CLLC resonant circuit is also investigated in this paper. In addition, a smooth mode transition is provided to avoid any transients caused by a rapid frequency change. The operating principles of the control system is analyzed extensively. Compared with the traditional Pulse-Frequency Modulated (PFM) resonant converters, the proposed control system attains enhanced efficiency even at extremely low voltage gains while sustaining a nearly consistent high-efficiency profile. Furthermore, it mitigates voltage regulation instability under light loads attributed to parasitic components by constraining the frequency spectrum. The proposed control system presents an appealing solution for high-voltage, high-power energy storage applications that demand a broad range of voltage gains and where the influence of switch RdsONis reduced due to the low current characteristics typical of these applications. Finally, a 500 W 100 V-350 V input, 96 V output prototype has been built to verify the performance of the proposed converter system.
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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 | 0.001 |
| 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".