Lumped Parameter Modeling of SAE J2601 Hydrogen Fueling Tests
Bibliographic record
Abstract
The safety of hydrogen storage is essential for the development of fuel cell vehicles. A mathematical model for a compressed hydrogen storage tank is established based on the mass conservation equation, the energy conservation equation and the real gas equation of state. Using the Matlab/Simulink platform, a dual-zone lumped parameter model, which divides the tank into a hydrogen gas zone and a tank wall zone, is established. The initial conditions of the MC Default method hydrogen filling from SAE J2601 are utilized in the lumped parameter model for numerical simulation. Five cases are studied, including two different tanks. One case used the Lookup table for hydrogen refueling, and four cases used the MC Default method for fueling. The hydrogen gas temperature, wall temperature, pressure in the tank and state of charge are obtained during the fueling process. The simulated results show that the dual-zone lumped parameter model can well predict the temperature, pressure and state of charge (SOC) for Type IV tanks with volumes of 249 L and 117 L during refueling. By using the averaged heat transfer coefficient (80 W/(m2·K)) between gas and wall, and the constant heat transfer coefficient (20 W/(m2·K)) between wall and environment, the gas temperature and pressure of our dual-zone lumped parameter model show good agreement with the experiment. The maximum difference between simulated and experimental wall temperatures for five cases is around 2 °C. The experimental wall temperatures were measured on the external surface of the tank, while the simulated wall temperature of the dual-zone lumped parameter model is representative of a mean temperature averaged alone with the radial direction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".