Efficiency Enhancement of an Open Cathode Fuel Cell Through a Systemic Management
Bibliographic record
Abstract
This paper addresses the design of a systemic management to improve the energetic efficiency of an open cathode proton exchange membrane fuel cell (PEMFC) in a hybrid system. Unlike the other similar works, the proposed approach capitalizes on the usage of both thermal management strategy and current control to meet the requested power from the system by the minimum fuel consumption. To do so, firstly, an experimentally based 3D mapping is performed to relate the requested power form the PEMFC to its operating temperature and current. Secondly, the reference temperature which leads to gaining the demanded power by the minimum current level is determined to minimize the hydrogen consumption. Finally, the temperature control is formulated by an optimized fuzzy logic scheme to reach the determined reference temperature by acting on the cooling fan of the PEMFC system, whilst the current is being regulated by its controller. The inputs of the fuzzy controller are the PEMFC current and temperature error and the sole output is the duty factor of the fan. The proposed methodology is tested on an experimental test bench to be better evaluated in a real condition. The obtained results from the proposed systemic management indicate promising enhancement of the system efficiency compared to a commercial controller. The proposed method of this work is extendable and applicable in fuel cell hybrid electric vehicles.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".