Thermal Management System for Hydrogen Fuel Cell Vehicle Based on Transcritical CO2 Heat Pump
Bibliographic record
Abstract
Carbon peaking and carbon neutrality goals have been set, and the Kigali Amendment to the Montreal Protocol has officially come into force. The vigorous development of hydrogen fuel cell vehicles has become an important means of energy conservation and emission reduction in the transportation sector. Nevertheless, current fuel cell vehicles have problems including a narrow operating temperature range, the greenhouse effect produced by the thermal management working fluid, and low efficiency owing to the independent thermal management system. Thus, based on a transcritical CO2 heat pump, a novel thermal management scheme was proposed that uses multiple control algorithms and combines the thermal management systems of vehicle cabins and proton exchange membrane fuel cells (PEMFC). AMESim was used to simulate this system. After a comprehensive analysis, the results showed that the integrated thermal management system can ensure that the cabin and battery quickly reach and maintain their respective ideal temperature. Compared with a traditional independent thermal management system, the new system has better energy utilization efficiency over the entire operating range, with greater overall efficiency and energy savings.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".