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Record W2040120068 · doi:10.1115/detc2010-28238

Design of an Anti-Idling System for Police Vehicles

2010· article· en· W2040120068 on OpenAlexafffund
Brian Su-Ming Fan, Amir Khajepour, Mehrdad Kazerani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicle emissions and performance
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPowertrainAutomotive engineeringBattery (electricity)Modular designFuel efficiencyScalabilityEngineeringCost reductionPower (physics)SimulationComputer scienceOperating system

Abstract

fetched live from OpenAlex

A configurable, modular, and flexible vehicle model utilizing scalable powertrain components has been developed at the University of Waterloo. The configurable vehicle model is modified to create an anti-idling system for police vehicles, where an additional battery is utilized to reduce the amount of engine idling time. The goal of the design study is to perform modeling and simulation of the anti-idling system from a financial cost point of view, in order to investigate the potential cost and fuel reduction over a conventional system. The cost function includes the total cost of the battery, the equivalent fuel consumption, and the carbon tax over a period of five years. It is concluded that the anti-idling system demonstrated significant fuel and cost reduction compared to one without. Furthermore, it is found that depending on the SOC threshold of the power management logic, the duration of time over which the engine is activated varied in a non-linear fashion. Future works include performing optimization of the power management logic and also investigates the effects of utilizing different battery types and sizes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score0.200

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.017
GPT teacher head0.238
Teacher spread0.220 · 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 designBench or experimental
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

Citations1
Published2010
Admission routes2
Has abstractyes

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