Light Duty Hybrid Vehicles - Influence of Driving Cycle and Operating Temperature on Fuel Economy and GHG Emissions
Bibliographic record
Abstract
Four light duty gasoline-electric hybrid vehicles (Toyota Prius, Honda Civic, Honda Insight and Ford Escape) were tested over five different driving cycles at two temperatures using chassis dynamometer emissions testing procedures. The vehicles were tested at 20degC and -18degC. Second-by-second gaseous emissions and total particle number emissions from the hybrid vehicles show patterns that are in some ways similar to conventional multiport fuel injected gasoline vehicles, with large increases in concentration on accelerations. Under driving conditions where the engine may be turned off and on, or under conditions where the electric drive assists in accelerations, different patterns are observed. These patterns can also differ from one repeat of a driving cycle to another, depending on the state of charge of the battery. Cold temperature operation is very demanding on the electric system of the hybrids. The batteries are challenged not only in starting the vehicle but in retaining charge during operation. These conditions result in much higher fuel consumption and mass emission rates of pollutants and greenhouse gases as compared to standard temperature operation and also greatly influence the transient nature of emissions from these vehicles.
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 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".