Design and control of isolated current‐fed DC–DC converters for fuel cell stacks EIS incorporating wide‐frequency‐range of perturbations
Bibliographic record
Abstract
Abstract Electrochemical impedance spectroscopy (EIS) performed by power converters makes in situ diagnostics possible for fuel cell stacks (FCS) in end applications without displacing and dismantling the stack. Previously published solutions, however, fall short of adequately generating a wide frequency range of perturbations covering all internal information of an FCS. The practical challenges and limitations of achieving converter generated perturbations at various frequencies are revealed in this paper. The high‐end frequencies are constrained by the converter control bandwidth, whereas the low‐end ones may cause significant ripples on the converter output. To address these issues, this paper proposes the use of isolated current‐fed DC–DC converters for the design of an FCS power converter considering the output characteristics of the FCS operating at desired conditions and incorporating wide‐frequency‐range of EIS perturbations. Moreover, the oscillations resulting from EIS operations are tackled by properly guiding them from the load side to a primary side energy storage. A fuel‐cell‐dedicated power conditioning converter capable of presenting a wide‐range frequencies of EIS perturbations is thus achieved. A design case is presented, and its simulation and experimental results verify the effectiveness of the proposed solution.
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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 | 0.000 | 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) | 0.001 | 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 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".