PEMFC operational and design strategies for sub-zero environments
Bibliographic record
Abstract
Proton Exchange Membrane Fuel Cells (PEMFCs) are increasingly considered as a replacement for the internal combustion engine. However, the behavior of PEMFCs in cold weather has yet to be fully understood, and this is a key requirement for automotive applications. This article provided a summary of approaches developed by Ballard to develop a PEMFC for sub zero environments with the objective of meeting automotive requirements. Various strategies were discussed, including heat generation and management, as well as material degradation prevention and the use of alternate materials less susceptible to freeze damage. Reactant starvation and methanol combustion were discussed. It was suggested that most of the phenomenon that need to be considered when PEMFCs are exposed to cold environments originate from the cathode reaction. Equations concerning heat losses were presented along with polarization curves. Issues concerning purging were also discussed and catalyst impregnation was reviewed, as well as the use of alternate materials such as ethylene glycol, methanol and perfluorocarbons. Examples applicable to operation using hydrogen, reformate and methanol were reviewed. Strategies to recover performance losses due to freezing were addressed and examples of supplementary benefits derived from strategies to reduce freeze damage were presented. It was concluded that more resources should be dedicated to modelling. In addition measurement methods to probe freezing issues such as ice quantification and localization should be developed, as well as the optimization of practical techniques. It was concluded that the fuel infrastructure plays a significant role, as freezing strategy choices are dependent to some extent on fuel selection. As more data are gathered and failure modes are identified, additional resources will be applied to address performance recovery and mitigation strategies. 60 refs., 1 tab., 23 figs.
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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".