Thermal management of polymer electrolyte membrane fuel cells: comparative assessment of cooling systems
Bibliographic record
Abstract
Thermal management of proton exchange membrane (PEM) fuel cells in hydrogen electric and hybrid electric vehicles is of great importance since most of heat generated inside the cells is absorbed by the structure of the fuel cells. Understanding the thermal behavior of PEM fuel cells helps in the design of effective cooling systems to dissipate the heat in the fuel cell and maintain the cell's temperature within the optimum operating range (i.e., 65 °C-75 °C). For the first time, we developed a methodology that combines thermal-electrochemical modeling of PEM fuel cell with empirical heat transfer correlations to reveal the temperature distribution through the structural layers of the PEM fuel cells. In addition, various cooling mediums (including air, hydrogen and water) flowing through the bipolar plate cooling channels are compared in terms of their cooling effects and the uniformity of the temperature distribution in the fuel cells at various flow conditions (i.e., different temperatures and coolant velocities). It is found that, although increasing the coolant flow velocity through the cooling channels enhances heat transfer between the fuel cell surface area and the coolant and reduces the average temperature of the PEM fuel cell, it results in lower temperature uniformity through the structure of the cells compared to lower coolant flow velocities. At a coolant temperature of 35 °C, the maximum temperature of the PEM fuel cell is 88 °C, 79 °C and 56 °C for air, hydrogen and water cooling mediums, respectively. In addition, at a coolant flow velocity of 0.02 m/s, the use of water in the cooling channels results in a temperature difference between the membrane with the highest temperature and the outer surface of the cell with the lowest temperature of 9 °C, while this temperature difference is about 6.5 °C when hydrogen is used as the coolant.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".