Design Criteria of Non-Isolated Bidirectional DC-DC Converters: A Review
Bibliographic record
Abstract
Battery energy storage systems are widely used for renewable power generation and electric transportation systems. Bidirectional DC-DC converters (BDCs) are key components in such systems, enabling bidirectional power flow in battery charging and discharging modes. BDCs can be categorized into isolated and non-isolated. Non-isolated BDCs have lower volume, weight, and power losses, suitable for compact structures without needing galvanic isolation. In this paper, a comprehensive literature review is conducted for non-isolated BDCs, covering soft switching, current ripple reduction, high voltage gain and resiliency techniques. Soft switching aims to reduce switching losses and improve efficiency, including auxiliary circuits and non-auxiliary methods, such as interleaved structures, phase-shift modulation, and synchronous rectification. Current ripple reduction focuses on capacitive loop configurations, interleaved structures, and coupled inductor-based methods. Batteries are low-voltage power sources, BDCs can increase the output voltage to a level required by the applications through an appropriate voltage gain, and high voltage gain techniques include capacitor-based, magnetic-based, and combined networks, and mixed structures. Resiliency is explored to ensure reliable operations under adverse conditions. This review provides valuable insights into developing more efficient, reliable, and high-performance BDCs, addressing the evolving demands of modern energy systems. Future research directions in non-isolated BDCs are recommended in this paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".