Dispenser reliability analysis for hydrogen refuelling stations in British Columbia
Bibliographic record
Abstract
The infrastructure development of hydrogen refuelling stations (HRSs) is the top challenge of the applications of hydrogen propulsion technologies on ground transportation. Dispenser reliability often affects user experience on refilling hydrogen fuel cell electric vehicles (HFCEVs). To assess and analyse dispenser reliability, the fuelling fault conditions that cause fuelling anomalies should be categorised and analysed based on the system operating conditions, so that the correlation between fuelling system behaviours characterised by irregular data patterns and dispenser reliability can be identified. Besides station operating conditions and physical process control in hydrogen fuelling to the HFCEVs, interfaces built on the dispenser for data communication are also important to achieve fast normal fills while fulfilling all the safety limits and process requirements. Table 1 lists and summarises all the relevant fuelling protocols and standards for the development of fuelling communication hardware and software. Fuelling protocols J2601, J2601-2 and J2601-3 provide guidance to HRS builders and manufacturers to fulfill performance requirements and control process limits. On the other hand, J2600 provides the requirements and guidance for designing, building and testing of fuelling connection devices, such fuelling nozzles and receptacles. J2799 imparts harmonized development and implementation of the hydrogen interfaces, specifying communications hardware and software requirements. With the instructions and recommendations of these standards, HRS infrastructure development encompassing various process control components is executed from blueprints to stations in service.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".