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Record W4402452648 · doi:10.11159/eee24.105

Innovative Integration Of Solar & Wind Energy For Future Automotive Propulsion Systems

2024· article· en· W4402452648 on OpenAlexvenueno aff
Cristian Helera, Dan Alexandru Stoichescu

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPropulsionAutomotive industryWind powerAerospace engineeringSolar energyAutomotive engineeringComputer scienceEngineeringSystems engineeringEnvironmental scienceAeronauticsElectrical engineering

Abstract

fetched live from OpenAlex

As the automotive industry development to achieve a sustainable future, the incorporation of renewable energy sources such as solar and wind power into automotive propulsion systems emerges as a promising solution.This scientific article delves into the efficient utilization of solar and wind energy in automotive applications, presenting a sustainable approach to future mobility.Extensive discussions follow on the individual integration of solar and wind energy in automotive systems, exploring existing technologies, their advantages, and limitations.The article places a significant focus on the combined integration of solar and wind energy in automotive propulsion systems.It explores innovative architectures and technologies designed to maximize energy generation, enhance system efficiency, and extend the driving range of vehicles.Furthermore, the article evaluates the performance of integrated solar and wind energy systems in automotive applications, comparing their effectiveness to traditional propulsion systems through empirical data and analysis [1][2][3][4][5][6][7][8][9][10][11][12][13].Results: The experimental results conducted with wind turbines mounted on the car reveal promising insights:1. 50W Commercial Wind Turbine Maximum recorded power: 50W at 120 km/h.• indicates normal operation under standard wind conditions.• potential for improved performance at higher speeds.2. Special 100W Turbine Achieved power: 110W at 120 km/h, exceeding estimated turbine power.• specifications and 3D technology enhance efficiency.3. Impact on Fuel Consumption Without turbine: 6.2 liters at 100 km/h.With turbine: slight increase to 6.3 liters at the same speed.• small impact on fuel consumption, suggesting improved efficiency with renewable energy and fuel combination.4. Turbine Positioning Positive impact on performance when turbines are installed on the roof and in front of the vehicle.• direct exposure to air speed and pressure enhances turbine performance.5. Solar Panel Results Sun in the clouds, solar panel connected for 10 minutes: -current: 4.86A, Voltage: 13.7V, Power: 66.582W.• reduced power due to inconsistent solar illumination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.007
GPT teacher head0.210
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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