On the Potentially Misleading Evaluation of CO<sub>2</sub> Emissions for Hybrid-Electric Vehicles
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The adoption of hybrid electric vehicles (HEVs) is becoming more popular during the last few years due to government incentives and favourable legislation both for automotive companies and final users. This type of vehicle claims very low carbon dioxide emissions while eliminating the range anxiety associated with battery electric vehicles thanks to the on-board range extender being able to recharge the battery throughout the journey. Unfortunately, the low emissions values are more representative of the particular mathematical model implemented by the legislation than the measured real driving emissions. Specifically, the legislation does not take into account the CO<sub>2</sub> embedded in production of the batteries or of the electrical energy stored in it. This work analyses these aspects by means of a numerical model of the BMW i3 94Ah vehicle. The results obtained are collected from simulations conducted over the Worldwide harmonized Light vehicles Test Cycle (WLTC) by using the commercial software GT-Power. The numerical model has been built with parameters presented in literature and validated against experimental data originally published as part of an Argonne National Laboratories research project. The model assessed all the most important energetic and emissions parameters over the regulated driving cycle such as the instantaneous fuel and electrical energy consumptions together with the CO<sub>2</sub> produced by the fuel, the instantaneous vehicle power and the total energy needed to complete the driving cycle. The standard CO<sub>2</sub> results are then compared with the same one also considering the CO<sub>2</sub> embedded in the battery. Finally, an extreme interpretation of the analysis illustrates the misleading approach taken by the legislation in the definition of the CO<sub>2</sub> emission for a HEV and discusses a more suitable way to evaluate them.</div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".