MétaCan
Menu
← Back to cohort

Preliminary Study of Battery Swap Stations for Electric Vehicles on Highways

2024· article· en· W4404563746 on OpenAlexaff
Daniele De Martini, Sofia Borgosano, Gianluca Mongiu, Michela Longo, Wahiba Yaïci, Seyed Mahdi Miraftabzadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsSwap (finance)Automotive engineeringBattery electric vehicleBattery (electricity)Computer scienceAutomotive batteryEnvironmental scienceTransport engineeringElectrical engineeringEngineeringBusinessPower (physics)

Abstract

fetched live from OpenAlex

As greenhouse emissions and pollution escalate, the need for new solutions to mitigate their impact becomes increasingly urgent. Electric vehicles (EVs) are viewed as a green alternative to traditional internal combustion engine vehicles, offering a means to alleviate pollution. However, they still pose limitations, particularly concerning range and charging capabilities. New charging methods have been introduced into the market in recent years, including Battery Swap Stations (BSS), which have not yet gained significant traction in Europe. This study assesses the feasibility of implementing BSS technology along highways in Italy. Focusing on the A4 highway from Turin to Trieste, the analysis utilizes technical data from three of Italy's top-selling electric cars. A model is employed to evaluate vehicle consumption on the highway, accounting for necessary recharge stops. Finally, a cost-benefit analysis is conducted to ascertain the feasibility of this charging solution.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.028
GPT teacher head0.301
Teacher spread0.273 · 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 designObservational
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

Explore more

Same topicAdvanced Battery Technologies Research→French-language works237,207→