Membrane Durability in Heavy Duty Fuel Cells for Transit Bus Applications
Bibliographic record
Abstract
Through a concerted research agenda involving Ballard Power Systems, Simon Fraser University, and University of Victoria, we are currently developing mechanistic understanding, models, and approaches to support durable and reliable fuel cell technology for transit buses with median lifetime equal to or better than incumbent diesel engines [1]. The research focuses on mitigation of membrane degradation, which has been identified by Ballard as a potential lifetime limiting failure mode leading to stack failure under bus operating conditions. Failure modes associated with gas leaks are predominantly caused by a combination of chemical and mechanical membrane degradation [2]. Due to the stringent product lifetime requirements for heavy duty applications and the complexity of the degradation mechanisms, durability evaluations and predictions are a significant challenge. An important goal of the present research program is to develop fundamental modeling tools in order to explore and understand the degradation mechanisms and to predict the membrane durability under conditions that are relevant for automotive applications. A specialized 1-D membrane electrolyte assembly (MEA) reaction-transport model was developed and integrated with a numerical algorithm for chemical membrane degradation via hydrogen peroxide mediated hydroxyl radical attack [3]. The time-dependent membrane dimensions and transport properties were calculated by considering the material and water losses caused by the chemical degradation. The model is thereby capable of simulating the chemical degradation rate, shown to be in good agreement with fluoride release and thinning data from accelerated stress tests. Using this model, we recently discovered an iron ion redox cycle in the MEA that controls the effect of cell voltage on membrane durability. Additionally, mechanical degradation was modelled using a finite element model with an elastic-viscoplastic constitutive relation that can simulate the stress and strain distributions in the MEA during hygrothermal variations such as wet/dry cycles [4]. This model was combined with a numerical framework for membrane fatigue, supported by experimental fatigue property measurements under cyclic tensile loadings, in order to estimate the fatigue lifetime distribution as a function of operating conditions and duty cycles. In addition to the fundamental models, an empirical approach was established by utilizing a specialized accelerated membrane durability test (AMDT) [5] to predict membrane lifetime during field operation of fuel cell buses. The proposed AMDT applies enhanced levels of stress, such as elevated voltage, temperature, and humidity cycling, to ensure membrane failures consistent with those observed in historical bus field trials but in a much shorter time. Acceleration factors for each stressor were determined and employed to develop an empirical lifetime prediction model based on the general log linear - Weibull stress life distribution. The empirical model focuses on extrapolating the lifetime of membranes under AMDT conditions to a lifetime expected under actual bus conditions. The predictive ability of the model was benchmarked with recent field data from the fuel cell bus fleet in Whistler, BC, and applied to predict the bus lifetime resulting from various lifetime enhancing strategies. REFERENCES http://www.apc-hdfc.ca/. Lim, et al., J. Power Sources 257 (2014) 102. Wong, et al., J. Electrochem. Soc. 161 (2014) F823; ChemSusChem 8 (2015) 1072. Khorasany, et al., J. Power Sources 252 (2014) 176; 274 (2015) 1208; 279 (2015) 55. Macauley, et al., J. Electrochem. Soc. 162 (2015) F98.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".