Fuel Cell Electrochemical Characterization before and after Break-in; Implications for End of Line Manufacturing Diagnostics
Bibliographic record
Abstract
Hydrogen fuel cells, particularly proton exchange membrane fuel cells (PEMFCs), are one of the leading technologies in the transition to clean energy. However, their manufacturing can be costly and time consuming, with one of the primary bottlenecks being end of line (EOL) quality assurance and quality control (QA/QC). First, before a PEMFC can have reliable and consistent performance, it must undergo a lengthy break-in procedure which can take several hours [1]. Furthermore, a cell or stack can have defects present before break-in, meaning that time and resources were wasted on a cell that was already faulty. Secondly, PEMFC stacks cannot be tested using potential controlled methods, only current controlled methods, meaning that many conventional electrochemical techniques such as cyclic voltammetry (CV) and linear sweep voltammetry (LSV) cannot be applied. This research aims to remedy these issues by developing novel stack compatible diagnostic methods that can detect PEMFC failures at EOL before break-in. While there has been significant research into developing PEMFC break-in methods, namely developing faster methods which yield higher performance cells, research has been scarce on the characterization of pre break-in cells [1]. As such, there is limited understanding of the properties of individual cell components before break-in as well as how their measured properties might correlate to those of a post broken in cell. We therefore aim to understand the characteristics of PEMFC components before break-in and what component changes can be seen after break-in. From this understanding, we hope to be able to both detect faulty cell components before break-in as well as predict certain performance parameters. QA/QC diagnostics will be validated by testing cells which have been fabricated with known defects. The effect of temperature and humidity on these diagnostics, before and after break-in, is investigated as well. Due to the lack of potential control over individual cells in a stack, current based electrochemical methods must be developed and used for in situ diagnostics. Currently, two methods are being investigated as alternatives to the commonly used potential controlled methods of CV and LSV. The first diagnostic method examined is the hydrogen/nitrogen concentration cell leak test described in [2], [3], which is a stack compatible alternative to LSV which quantifies the hydrogen gas crossover rate from anode to cathode at open circuit potential. The second method is the galvanostatic current sweep described in [4], [5] as an alternative to CV for ECSA characterization. While both of these methods have been proven in practice, they have yet to be tested in larger stacks which have been freshly manufactured. Therefore, these methods will be performed on cells before and after break-in under different humidity and temperature regimes to assess their applicability to EOL diagnostics and defect detection. Acknowledgements This work was supported by the Natural Sciences and Engineering Research Council of Canada, Mitacs, Greenlight Innovation, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, Pacific Economic Development Canada, and Canada Research Chairs. References [1] F. Van Der Linden, E. Pahon, S. Morando, and D. Bouquain, “A review on the Proton-Exchange Membrane Fuel Cell break-in physical principles, activation procedures, and characterization methods,” Journal of Power Sources, vol. 575, p. 233168, Aug. 2023, doi: 10.1016/j.jpowsour.2023.233168. [2] A. M. Niroumand, O. Pooyanfar, N. Macauley, J. DeVaal, and F. Golnaraghi, “In-situ diagnostic tools for hydrogen transfer leak characterization in PEM fuel cell stacks part I: R&D applications,” Journal of Power Sources, vol. 278, pp. 652–659, Mar. 2015, doi: 10.1016/j.jpowsour.2014.12.093. [3] A. M. Niroumand, H. Homayouni, G. Goransson, M. Olfert, and M. Eikerling, “In-situ diagnostic tools for hydrogen transfer leak characterization in PEM fuel cell stacks part III: Manufacturing applications,” Journal of Power Sources, vol. 448, p. 227359, Feb. 2020, doi: 10.1016/j.jpowsour.2019.227359. [4] K.-S. Lee et al., “Development of a galvanostatic analysis technique as an in-situ diagnostic tool for PEMFC single cells and stacks,” International Journal of Hydrogen Energy, vol. 37, no. 7, pp. 5891–5900, Apr. 2012, doi: 10.1016/j.ijhydene.2011.12.152. [5] E. Brightman, G. Hinds, and R. O’Malley, “In situ measurement of active catalyst surface area in fuel cell stacks,” Journal of Power Sources, vol. 242, pp. 244–254, Nov. 2013, doi: 10.1016/j.jpowsour.2013.05.046.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".