Development of a fuel cell simulator based on an experimentally derived model
Bibliographic record
Abstract
Fuel cells (FC) are power sources that convert electrochemical energy into electrical energy in a clean and efficient manner. FC technology presents one of the most promising solutions to reduce fossil fuel consumption. FC simulators are expected to play a key role in the development of FC systems due to their low cost and flexibility. This paper presents the development of a stand-alone FC simulator based on a low cost digital signal processor (DSP). The proposed fuel cell simulator emulates the electrical dynamic behavior of a direct methanol fuel cell (DMFC) stack. This is achieved via a power converter with a robust control strategy with fast dynamic response. The reference signal for the power converter that represents the dynamic behavior of the FC is generated from an experimental model of the DMFC that can be extended to other FCs. The output of the FC simulator is a reproduction of the dynamic behavior of DMFC model, thus emulating a FC stack. Test results of the FC simulator and the actual FC are compared and discussed. From the viewpoint of educational purposes, the proposed FC simulator provides a flexible solution at a low cost for student and engineers in training. In addition, the proposed simulator can be used for the design and development of FC power electronics
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".