A novel design and feasibility analysis of a fuel cell plug-in hybrid electric vehicle
Bibliographic record
Abstract
Hydrogen powered fuel cell vehicles (FCVs) are receiving global attention, stimulated by the urgent need for more fuel-efficient vehicles. However, current challenges for fuel cells such as high cost, sizing problem, and limited driving range, greatly affect the pace of FCV development. At the same time, domestic and renewable energy resource usage is frequently being encouraged for future electric propulsion applications. This philosophy has lead auto manufacturers to investigate the potential of plug-in hybrid electric vehicles (PHEVs). In this paper, a fuel cell based PHEV (FC-PHEV) configuration, powered by combining an on-board regenerative fuel cell (RFC) and down-sized Ni-MH batteries, is modeled and investigated in detail. This configuration prospectively points towards remarkable advantages from the point of view of both environmental as well as cost friendliness. In addition, an FC-PHEV will also depict longer life span, while maintaining good vehicle performance, compared to regular plug-in hybrid vehicles (PHEVs) or fuel cell hybrid electric vehicles (FC-HEVs). This paper will present a power train configuration of a FC-PHEV, developed for a family sedan. A suitable power management approach has been developed, which considers fuel economy, component efficiency, and driving pattern. The vehicle performance and feasibility are also investigated based on the modeling and simulation studies. A detailed comparison and discussion from the point of view of fuel economy, drive train efficiency, as well as practical cost and commercialization issues will also be presented.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".