Evaluation of Fuel Consumption and Dynamic Performance of a Compressed Natural Gas Hybrid Electric Truck
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The objective of the project was to compare the fuel consumption of a prototype hybrid electric CNG truck with that of two trucks: a CNG truck and a diesel truck for the similar market and operating conditions. The tests were conducted on a test route representative of the conditions encountered by these vehicles in normal driving operations. The test route length was 276 km with a maximum altitude difference of 374 m. The test route had four sections, including a hilly section with a length of 88 km. The result of the comparison between the two CNG trucks was expressed as fuel savings of CNG in percentage. The fuel consumption of the diesel truck was accurately measured using the gravimetric method. The hybrid electric CNG truck showed average fuel savings of 3.6% and demonstrated up to 7.7% in savings for the entire trip compared to the CNG truck. On the hilly section of the route, the hybrid electric CNG truck showed average fuel savings of 22% for the ascending portion and 9.7% for the complete section. On this section, the hybrid electric CNG truck also demonstrated superior dynamic behavior compared to the other two trucks. The torque and power contribution of the electric axle led to a less demanding and more economical behavior for the hybrid electric truck engine on the ascending section of the hilly route. The hybrid electric truck took a shorter time to complete the hilly section of the route compared to the other two trucks.</div></div>
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".