Integrated Thermal Management for Hydrogen Fuel Cell Buses Through Maisotsenko-Cycle Cooling and Water Recycling
Bibliographic record
Abstract
The electrification of transportation is rapidly transforming public mobility, yet batteryelectric vehicles still face significant range limitations.In this context, hydrogen fuel cell systems emerge as a promising alternative, capable of extending vehicle range while reducing environmental impact.However, thermal management remains a critical challenge, as passenger cabin cooling and the regulation of fuel cell and battery temperatures consume a substantial portion of the vehicle's available energy.This study explores the use of an evaporative cooling system based on the Maisotsenko cycle to cool the passenger cabin of a fuel cell bus while also contributing to powertrain thermal management through the reuse of exhaust airflow.In the proposed configuration, the primary airflow from the evaporative cycle is directed toward cabin ventilation and cooling, whereas the working exhaust air is utilized for the thermal regulation of the fuel cell and battery system.Additionally, the water produced as a byproduct of the hydrogenoxygen reaction in the fuel cell is recovered and used to sustain the evaporative cooling process, creating an integrated system that reduces dependence on external resources and minimizes overall energy consumption.The system's performance was evaluated in terms of cooling capacity, water balance, and thermal efficiency under various environmental conditions.Results indicate that this synergistic approach can significantly enhance the energy efficiency of hydrogen fuel cell electric buses, providing an innovative and sustainable solution for urban mobility.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".