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Record W2057965675 · doi:10.1109/itec.2014.6861868

Development of a hybrid energy storage system (HESS) for electric and hybrid electric vehicles

2014· article· en· W2057965675 on OpenAlexaff
Kun Zhuge, Mehrdad Kazerani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDrivetrainAutomotive engineeringElectrificationEnergy storagePropulsionBattery (electricity)Automotive industryInternal combustion engineHybrid vehicleCapacitorEngineeringElectric motorComputer scienceSupercapacitorElectrical engineeringPower (physics)TorqueVoltageElectricityAerospace engineering

Abstract

fetched live from OpenAlex

Extensive use of internal combustion engine (ICE)-based vehicles has contributed to severe adverse impacts on the environment and accelerated depletion of fossil fuel reserves, leading to considerable rise in price of gas over the past two decades. These challenges, plus the low efficiency associated with the conventional drivetrains, have made the automotive industry seriously consider and move towards drivetrain electrification in vehicular systems. In electrified vehicles, the propulsion is fully or partially provided by electric motors, powered by onboard energy storage systems. In an attempt to make up for the limitations of the existing energy storage devices and contribute to vehicle electrification movement, this paper examines the feasibility and capability of a hybrid energy storage system (HESS), composed of battery and ultra-capacitor units, through simulation and experimentation using a laboratory prototype. The choice of HESS topology has been made based on simplicity of power and control circuits, cost and performance. The design takes into consideration the required power, the converter losses, limitations of energy storage devices, and quality of the current drawn from battery cells. Experimental results are provided to verify the analytical expectations and simulation results.

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 score0.663
Threshold uncertainty score0.867

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 head0.007
GPT teacher head0.181
Teacher spread0.175 · 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

Citations16
Published2014
Admission routes1
Has abstractyes

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