Life Cycle Assessment of Vehicle Lightweighting: Novel Mathematical Methods to Estimate Use-Phase Fuel Consumption
Bibliographic record
Abstract
Lightweighting is a key strategy to improve vehicle fuel economy. Assessing the life-cycle benefits of lightweighting requires a quantitative description of the use-phase fuel consumption reduction associated with mass reduction. We present novel methods of estimating mass-induced fuel consumption (MIF) and fuel reduction values (FRVs) from fuel economy and dynamometer test data in the U.S. Environmental Protection Agency (EPA) database. In the past, FRVs have been measured using experimental testing. We demonstrate that FRVs can be mathematically derived from coast down coefficients in the EPA vehicle test database avoiding additional testing. MIF and FRVs calculated for 83 different 2013 MY vehicles are in the ranges 0.22-0.43 and 0.15-0.26 L/(100 km 100 kg), respectively, and increase to 0.27-0.53 L/(100 km 100 kg) with powertrain resizing to retain equivalent vehicle performance. We show how use-phase fuel consumption can be estimated using MIF and FRVs in life cycle assessments (LCAs) of vehicle lightweighting from total vehicle and vehicle component perspectives with, and without, powertrain resizing. The mass-induced fuel consumption model is illustrated by estimating lifecycle greenhouse gas (GHG) emission benefits from lightweighting a grille opening reinforcement component using magnesium or carbon fiber composite for 83 different vehicle models.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".