Simplified Implementation of Optimized Triple Phase Shift Control for DAB Converters in Electric Vehicles
Bibliographic record
Abstract
Dual active bridge (DAB) converter is a fully-isolated bidirectional DC-DC converter widely used for electric vehicle applications. DAB converter control primarily relies on phase shift control and advanced control techniques with multiple degrees of freedom have been used for optimal converter performance. This work presents a framework to optimize triple phase shift (TPS) control for loss minimization in DAB converters and a simplified control architecture to implement the optimal control with fast and stable dynamic response. Optimized control techniques often calls for look-up table (LUT) based approaches or regression model based approaches for control implementation, which results in complex control architectures with significant utilization of controller resources. The proposed control system in this work minimizes the L UT usage into a single L UT with a simplified linear model to input the optimal pulse widths. A feedback control system with improved dynamic performance is used to input the optimal phase shift. Compared to previous works, the proposed method reduces controller memory usage and computational time, while allowing for a simplified implementation. The proposed control system is verified using a simulation based study for a DAB converter in an electric vehicle application rated at 610–850 VI 48 VI 3.6 kW through efficiency and controller resource comparison, and dynamic analysis.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".