A superior hybrid fuel cell vehicle solution for congested urban areas
Bibliographic record
Abstract
This paper presents a promising solution to the problem of the bad environmental impacts and the high operating cost of the Internal Combustion Engine (ICE) gasoline powered vehicles. In Toronto, the capital of Ontario, the traffic jam is becoming a regular flavor of every day's commute that make the driving pattern featured with low speed and a lot of stops and goes. This driving pattern increases the pollution problem that already exists due to the large number of vehicles in Toronto streets and reduces the lifetime of the engine and the brakes leading to a more running cost. This study investigates the performance of a hybrid Fuel Cell (FC)/battery vehicle configuration, which is considered as one of the most promising clean vehicles in comparison with the traditional ICE vehicles. In this study, a model of an ICE mid-size vehicle was developed and validated against experimental acceleration tests. The ICE vehicle model was modified by replacing the ICE power-train with a FC and battery power-train while keeping the other vehicle parameters the same. A comparison between ICE and hybrid FC/battery vehicle configurations was conducted using a representative alignment load cycle in Toronto. It was found that the hybrid FC/battery configuration is much better than the ICE version in terms of emission, fuel economy, efficiency and speed tracking error due to the faster response of the control system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".