Environmental and Economic Benefits of a Battery Electric Vehicle Powertrain with a Zinc–Air Range Extender in the Transition to Electric Vehicles
Bibliographic record
Abstract
Emissions and pollution from the transportation sector due to the consumption of fossil fuels by conventional vehicles have been negatively affecting the global climate and public health. Electric vehicles (EVs) are a cleaner solution to reduce the emission and pollution caused by transportation. Lithium-ion (Li-ion) batteries are the main type of energy storage system used in EVs. The Li-ion battery pack must be considerably large to satisfy the requirement for the vehicle’s range, which also increases the cost of the vehicle. However, considering that most people use their vehicles for short-distance travel during daily commutes, the large pack is expensive, inefficient and unnecessary. In a previous paper, we proposed a novel EV powertrain design that incorporated the use of a zinc–air (Zn–air) battery pack as a range-extender, so that a smaller Li-ion pack could be used to save costs. The design and performance aspects of the powertrain were analyzed. In this study, the environmental and economic benefits of the proposed dual-battery powertrain are investigated. The results from the new powertrain were compared with values from a standard EV powertrain with one large Li-ion pack and a conventional internal combustion engine vehicle (ICEV) powertrain. In addition, an air pollution model is developed to determine the total amount of pollution released by the transportation sector on Highway 401 in Ontario, Canada. The model was then used to determine the effects of mass passenger EV rollout on pollution reduction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".