Implications of Driving Patterns on Well-to-Wheel Performance of Plug-in Hybrid Electric Vehicles
Bibliographic record
Abstract
This study examines how driving patterns (distance and conditions) and the electricity generation supply interact to impact well-to-wheel (WTW) energy use and greenhouse gas (GHG) emissions of plug-in hybrid electric vehicles (PHEVs). The WTW performance of a PHEV is compared with that of a similar (nonplug-in) gasoline hybrid electric vehicle and internal combustion engine vehicle (ICEV). Driving PHEVs for short distances between recharging generally results in lower WTW total and fossil energy use and GHG emissions per kilometer compared to driving long distances, but the extent of the reductions depends on the electricity supply. For example, the shortest driving pattern in this study with hydroelectricity uses 81% less fossil energy than the longest driving pattern. However, the shortest driving pattern with coal-based electricity uses only 28% less fossil energy. Similar trends are observed in reductions relative to the nonplug-in vehicles. Irrespective of the electricity supply, PHEVs result in greater reductions in WTW energy use and GHG emissions relative to ICEVs for city than highway driving conditions. PHEVs charging from coal facilities only reduce WTW energy use and GHG emissions relative to ICEVs for certain favorable driving conditions. The study results have implications for environmentally beneficial PHEV adoption and usage patterns.
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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 | 0.001 |
| 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 | 0.001 | 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".