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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:

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

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 teacher head, 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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