Optimizing Electric Mobility: A Multi-Criteria Decision-Making Approach for Sustainable Future of Electric Vehicles Through Smart Motor Choices
Bibliographic record
Abstract
To decrease reliance on fossil fuels and carbon emissions, electric vehicles (EVs) have emerged as indispensable instruments in the automotive industry's shift toward more sustainable methods.The achievement of peak efficiency in EVs is contingent upon the appropriate selection of motors and batteries; therefore, exploring methods that ensure sustainable decisions is imperative.The article employs Multi-Criteria Decision-Making (MCDM) techniques to assess and propose the most environmentally friendly amalgamation of batteries and motors for EVs.The research investigates many sustainability-related factors, encompassing energy density, power density, cost, longevity, and environmental impact.By employing MCDM methodologies, including the Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), a comprehensive decision framework is developed with an emphasis on sustainability.The research outcomes provide a thorough understanding of the trade-offs between battery life and motor efficiency, which holds significance for policymakers, academics, and manufacturers dedicated to endorsing sustainable energy practices.The suggested methodology not only streamlines the decision-making process but also promotes the development of environmentally sustainable and highly efficient EVs.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".