Feasibility of Recharging Electric Vehicles With Photovoltaic Solar Panels
Bibliographic record
Abstract
Abstract There are many reasons for the development and the use of renewable energy sources, such as the public awareness in the fight against climate change, energy independence with the security of supply, national competitiveness, technological development and job creation in a sector that has a great future. In this line, and within the proposed electric vehicle sustainability, it is an alternative to achieve a reduction of pollutant emissions and to increase the efficiency of road transport. The article presents a study of the use of electric vehicles from different points of view. It has been compared combustion vehicles with the electric counterparts in terms of power and features appreciated by the user in the automobile market. The purpose of the study was to analyze the feasibility to recharge different electric vehicles by solar photovoltaic modules, so that energy generation would not contribute to any CO2 emissions, when the system would be installed and ready to supply these vehicles. The study also shows a comparative analysis of the cost of purchasing electricity to the distributor compared with the using of a photovoltaic system designed to recharge the vehicle, even it has also been calculated the depreciation. Finally, it has been analyzed comparatively the type of the solar photovoltaic system considered more economically viable for recharging a pure electric vehicle (EV) therefore it has been compared projects on houses and on a parking to recharge several vehicles.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".