An Auxiliary Converter for Fuel Cell Stack EIS using a Switched-Capacitor Converter Interfaced Supercapacitor as a Bidirectional Energy Buffer
Bibliographic record
Abstract
Electrochemical impedance spectroscopy (EIS) is a promising tool for characterizing fuel cells. It was traditionally only applied to single cell or short stacks at low-power levels but was recently brought to high-power stacks due to a growing interest in performing in situ EIS without dismantling the stack. Converter-based EIS provides attractive solutions for this purpose. In this paper, an auxiliary EIS converter solution using a bidirectional energy buffer module composed of a switched-capacitor converter (SCC) and a supercapacitor is proposed. The module is designed towards having a more compact auxiliary converter unit. The design of the proposed module is investigated in detail and a comprehensive guideline is provided considering the probable limitations imposed by high EIS frequencies on this solution. The proposed system helps with the compactness and miniaturization of the entire auxiliary EIS converter and eliminating the potential problems of electrolytic capacitors such as bulkiness and limited lifetime due to impact of ripples. The effectiveness of the developed system is verified by simulation results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".