Extended Range Electric Vehicle Powertrain Simulation, and Comparison with Consideration of Fuel Cell and Metal-Air Battery
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The automobile industry has been undergoing a transition from fossil fuels to a low emission platform due to stricter environmental policies and energy security considerations. Electric vehicles, powered by lithium-ion batteries, have started to attain a noticeable market share recently due to their stable performance and maturity as a technology. However, electric vehicles continue to suffer from two disadvantages that have limited widespread adoption: charging time and energy density. To mitigate these challenges, vehicle Original Equipment Manufacturers (OEMs) have developed different vehicle architectures to extend the vehicle range.</div><div class="htmlview paragraph">This work seeks to compare various powertrains, including: combined power battery electric vehicles (BEV) (zinc-air and lithium-ion battery), zero emission fuel cell vehicles (FCV)), conventional gasoline powered vehicles (baseline internal combustion vehicle), and ICE engine extended range hybrid electric vehicle. The parameters of comparison are: energy consumption, range, life cycle and tailpipe emissions, cost, and customer acceptance. A unique zinc-air battery model was developed using the vehicle modelling software to perform the analysis, with consideration of research data, current market status, and controls logic of the dual energy systems powertrain.</div><div class="htmlview paragraph">Modelling of the five powertrains was performed using the vehicle modelling software Autonomie. In correspondence with the EcoCar 3 competition [<span class="xref">1</span>], the 2015 Chevrolet Camaro was used as the vehicle architecture platform. A powertrain decision matrix was developed to compare these powertrains from the metrics of energy consumption, emissions, customer acceptance, and life cycle cost. Emissions analysis is completed as a ‘Well-to-Wheel’ analysis in order to take into account all sources of emissions production.</div><div class="htmlview paragraph">As expected, all powertrains devoid of a gasoline internal combustion engine had lower tailpipe and greenhouse gas emissions. Powertrains powered by battery power alone, however, were not able to achieve the total range target, but it will be shown that developments in the metal-air battery will aid in addressing this limitation.</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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".