Flexible Power-Sharing Control Strategy for Triple-Active-Bridge DC–DC Converter With Fast-Dynamic Response
Bibliographic record
Abstract
Triple-active-bridge (TAB) dc-dc converter is a good candidate for multiple applications such as renewable energy, electric, and fuel cell vehicles due to its capability of integrating different sources and loads into a single system. Furthermore, it is essential to have a robust control strategy to obtain better dynamic performance for this converter. However, the realization of a fast-dynamic response using the traditional control approaches of the TAB dc-dc converter is hard due to the coupling among the three inductances of each port. Thus, modulation calculation and the model-based control design become very difficult. As a solution, the TAB dc-dc converter can be modified to a three-port dual-active-bridge (DAB) dc-dc converter, and the power coupling among the ports can be significantly reduced. Then, this article proposes a flexible power-sharing control strategy for the TAB dc-dc converter based on the combination of model-based feedforward compensation and output voltage feedback control strategy. Moreover, the stability analysis of the proposed control strategy is presented to verify the robustness of the control scheme in different conditions. In addition, the design principle of the PI parameters is provided for this converter. Finally, the effectiveness of the proposed control strategy and the accuracy of the PI parameters design are validated using simulation results and experimental results obtained from a small-scale hardware prototype.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".