Decoupled Power Flow in Triple Active Bridge Converter with PI and Decoupling-Matrix Control
Bibliographic record
Abstract
The Triple Active Bridge (TAB) converter is a promising solution for simultaneous electric vehicle (EV) charging due to its multi-port capability and galvanic isolation. However, crosscoupling effects between ports can compromise stability and power transfer accuracy. This paper proposes a novel hybrid control strategy integrating a PI controller with a decoupling matrix to address these challenges. The decoupling matrix, derived from a small-signal model of the TAB converter, dynamically adjusts phase-shift angles to enable independent power flow regulation across ports. While standalone decoupling methods exhibit transient fluctuations, the hybrid approach enhances stability and dynamic performance by combining model-based decoupling with closed-loop PI control. Detailed simulations validate the strategy, demonstrating significant reductions in cross-coupling effects alongside improved transient response and operational stability. The results highlight the robustness of the hybrid control under varying load conditions, making it a practical solution for next-generation EV charging systems. This work advances the development of control frameworks for multi-port converters in renewable energy integration and smart grid applications.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".