Topologies and Control Schemes of Bidirectional DC–DC Power Converters: An Overview
Bibliographic record
Abstract
Bidirectional DC-DC power converters are increasingly employed in diverse applications whereby power flow in both forward and reverse directions are required. These include but not limited to energy storage systems, uninterruptable power supplies, electric vehicles, and renewable energy systems, to name a few. This paper aims to review these converters from the point of view of topology as well as control schemes. From the point of view of topology, these converters are divided into two main categories, namely non-isolated and isolated configurations. Each category is divided into eight groups along with their respective schematics and a table of summary. Furthermore, the common control schemes and switching strategies for these converters are also reviewed. Some of the control schemes are typically applied to all DC-DC power converters such as PID, sliding mode, fuzzy, model predictive, digital control, etc. In this context, it should be noted that some switching strategies were designed specifically for isolated bidirectional DC-DC converters in order to improve their performance such as single phase shift, dual phase shift, triple phase shift, etc. The features of each topology and control scheme along with their typical applications are discussed, in order to provide a ground of comparison for realizing new configurations or finding the appropriate converter for the specific application.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".