Development of an analytical model of automobile energy consumption during use-phase for parametrized life cycle assessment
Bibliographic record
Abstract
Models for automobile energy consumption calculations often lack adaptability, granularity, and consistency, limiting the transparency, reproducibility, and representativeness of automobile processes in Life Cycle Assessment (LCA). Although developing parametrized models appears to be promising, their application to automobile energy consumption is constrained by the complexity of powertrain modeling and the integration of driving conditions. This work presents a model for gasoline and electric vehicles based on parametrized equations, describing physical drivers of energy demand while uncoupling the role of contributors, including the vehicle body, powertrain, path, and driver. An innovative method for parametrizing driving conditions is introduced, eliminating reliance on traditional driving cycles. Complemented by pre-set configurations to enhance usability, the computational tool PETRAUL built on this framework enables practitioners to perform precise and representative energy consumption calculations for vehicles. This study further demonstrates the tool's utility for both foreground and background LCA processes. This includes scenario analyses emphasizing the necessity of multi-solution strategies, a comparison with ecoinvent and Carculator highlighting improved granularity, and an LCA case study on lightweighting, illustrating enhanced representativeness for assessments across diverse technological and regional conditions. This streamlined LCA of a polycarbonate glazing highlights the potential burden shifting from the vehicle use phase to the manufacturing of lightweight materials, notably when coupled with electrification. Ultimately, PETRAUL provides a robust foundation for advancing LCA practices by enhancing adaptability and transparency in parametrized modeling, while illustrating the need for both technological and sobriety measures to reduce environmental impacts of the automobile industry. • Parametrized model for automobile energy consumption including driving conditions. • Model consistent for ICEVs and BEVs. • Tool with pre-set configurations for representative energy consumption computation. • Model tested in case studies and compared to LCA database processes. • Lightweighting relevancy hinges on material, technology, region and driving context.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".