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Record W2048486472 · doi:10.5539/mas.v2n4p122

Analysis and Design of Storage Battery Charge/Discharge Equalization Management

2008· article· en· W2048486472 on OpenAlexvenueno aff
Jiaqi Wang

Bibliographic record

VenueModern Applied Science · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsEqualization (audio)Battery (electricity)Charge cycleTrickle chargingCharge (physics)Computer data storageComputer scienceProcess (computing)Power (physics)Electrical engineeringVoltageAutomotive engineeringEngineeringTelecommunicationsComputer hardwarePhysics

Abstract

fetched live from OpenAlex

This article introduces the design of storage batter charge/discharge equalization system of the electrical cars based on HT46R47. Because it becomes into one difficulty in the development of electrical cars that the characters of the battery decides the equalization of the battery electric quantity, so this system adopts the equalization management to the storage battery charge/discharge, adjusts the unsuited batteries (over charge/over discharge) in the initial adjustment process, and implements equalization in the later charge process. The system makes the use capacity and the cycle life of the storage battery fully enhance. At the same time, this charger adopts the SCM as the main control component, which has many advantages such as simple and credible circuit, short charge time, low power consumption, low use trouble rate and so on.

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 score0.005
Threshold uncertainty score0.017

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.210
Teacher spread0.193 · 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
Published2008
Admission routes1
Has abstractyes

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