Examining the influence of battery sizing on hydrogen fuel cell – battery hybrid rail powertrains (hydrail) for regional passenger railway transport using dynamic component models
Bibliographic record
Abstract
To address the transportation sector’s contribution to climate change problems across North America (NA), passenger rail is an attractive solution. However, NA passenger rail traditionally relies on diesel motive power, which has been associated with causing health problems of noise, vibrations, and emissions. High costs of overhead and (or) third rail infrastructure have mostly precluded electrification. This paper examines the impact of battery size on fuel cell stack efficiency for hydrogen fuel cell – battery hybrid (hydrail) railway propulsion systems using dynamic simulations as opposed to existing simulations in the literature that rely on static efficiency values. The journey of the British Rail Class 156 diesel multiple unit is simulated over the round trip from Trehafod to Treherbert (UK) using a series hybrid architecture powertrain. Dynamic simulations at incremental battery masses were used to assess fuel cell efficiency, maximum power, and overall hydrogen consumption. Battery mass is employed as a proxy for power and energy capability of the battery. Results suggest that hydrail passenger railway systems work well, with hydrogen fuel cells handling most load dynamics. Hybridization with batteries works best and reduces fuel cell stack size and hydrogen consumption, with overall 64% stack efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".