Control Techniques With Low Computational Burden for a DAB-Based Two-Stage DC–DC EV Charging Converter System
Bibliographic record
Abstract
In the future, electric vehicle (EV) charging dc-dc converter system are usually required for realizing bidirectional operation, electric isolation, and large voltage range, which can be realized at the dc stage. However, because of unavoidable reactive power, the typical bidirectional and isolated dc-dc converters such as dual-active-bridge (DAB) dc-dc converter and LC-based resonant dc-dc converter usually generate large current stress at wide-range voltage. Thus, the design cost for high-power applications will inevitably be expensive. Consequently, this paper, combining a 3-level buck-boost stage, adopts a DAB-based two-stage dc-dc converter with a low current stress. Then, the simple control schemes, including the soft start-up method, the linear current-control-based inner loop technique, and the soft shutdown method, are proposed for the charging of EV. Especially, based on the naturally transient behavior, the linear current-control-based inner loop technique is presented with low computational burden and without instability concern. The proposed linear technique can easily and steadily realize the charging requirements such as constant current, power, and voltage charging. Besides, the presented method has no current and voltage shoots during transient process, which can significantly enhance charging reliability. Moreover, the changing rate of a charging current can be consistent and controllable to fulfill batteries' charging requirement. Finally, experiment results are provided to verify the effectiveness of the proposed techniques.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".