A Well-to-Wheel Comparison of Several Powertrain Technologies
Bibliographic record
Abstract
In order to evaluate the potential of several powertrain configurations, a well-to-wheel analysis is performed. Specifically, downsizing / supercharging and variable valve timing is examined and compared against other alternative vehicle concepts. In order to have a fair comparison, each powertrain configuration was added to a base vehicle, such that each vehicle had the same range, the same physical characteristics and similar performance. Upstream energy use and greenhouse gases were calculated with GREET 1.5a and the downstream energy use and greenhouse gases with ADVISOR 3.2. By downsizing / supercharging and adding variable valve timing, a spark ignition internal combustion engine can have comparable downstream overall efficiency, energy use, and greenhouse gas emissions, to a Diesel internal combustion engine. Analysis of the total energy use shows that efficiency improvements for an internal combustion engine should be made on the downstream stage (engine) while efficiency improvements for electric vehicle should be made on the upstream stage (electricity generation). Also, it was found that internal combustion engines are relatively insensitive to mass change compared to improvements in engine efficiency.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".