Next Generation Bipolar Plates for Automotive PEM Fuel Cells
Bibliographic record
Abstract
The results of a successful U.S. Department of Energy (DoE) funded two-year $2.9 MM program lead by GrafTech International Inc. (GrafTech) are reported and summarized. The program goal was to develop the next generation of high temperature proton exchange membrane (PEM) fuel cell bipolar plates for use in transportation fuel cell applications operating at temperatures up to 120 °C. The bipolar plate composite developed during the program is based on GrafTech’s GRAFCELL resin impregnated flexible graphite technology and makes use of a high temperature Huntsman Advanced Materials resin system which extends the upper use temperature of the composite to the DoE target. High temperature performance of the new composite is achieved with the added benefit of improvements in strength, modulus, and dimensional stability over the incumbent resin systems. Other physical properties, including thermal and electrical conductivity of the new composite are identical to or not adversely affected by the new resin system. Using the new bipolar plate composite system, machined plates were fabricated and tested in high temperature single-cell fuel cells operating at 120 °C for over 1100 hours by Case Western Reserve University. Final verification of performance was done on embossed full-size plates which were fabricated and glued into bipolar plates by GrafTech. Stack testing was done on a 10-cell full-sized stack under a simulated drive cycle protocol by Ballard Power Systems. Freeze-thaw performance was conducted by Ballard on a separate 5-cell stack and shown to be within specification. A third stack was assembled and shipped to Argonne National Laboratory for independent performance verification. Manufacturing cost estimate for the production of the new bipolar plate composite at current and high volume production scenarios was performed by Directed Technologies Inc. (DTI). The production cost estimates were consistent with previous DoE cost estimates performed by DTI for the DoE on metal plates. The final result of DTI’s analysis for the high volume manufacturing scenario ($6.85 /kW) came in slightly above the DoE target of $3 to $5/kW. This estimate was derived using a “Best Case Scenario” for many of the production process steps and raw material costs with projections to high volumes. Some of the process improvements assumed in this “Best Case Scenario” including high speed high impact forming and solvent-less resins, have not yet been implemented, but have a high probability of potential success.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".