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Record W2086633088 · doi:10.1145/1497561.1497572

Battery voltage modeling for portable systems

2009· article· en· W2086633088 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM Transactions on Design Automation of Electronic Systems · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBattery (electricity)Computer scienceVoltageAutomotive engineeringState of chargeElectrical engineeringSimulationPower (physics)Engineering

Abstract

fetched live from OpenAlex

Limited battery life imposes stringent constraints on the operation of battery-powered portable systems. During battery discharge, the battery voltage decreases, until a certain cutoff value is reached, marking the end of battery life. The amount of discharge capacity and energy delivered by the battery during its life depends not only on the battery characteristics, but also on the load conditions. A different system design may result in a different battery current (load) profile over time, leading to a different battery voltage profile over time. This article presents an analytical model that relates the battery voltage to the battery current, thus facilitating system design optimizations with respect to the battery performance. It captures well-known nonlinear phenomena of capacity loss at high discharge rates, charge recovery, and capacity fading. The proposed model has been validated against measurements taken on Li-ion batteries. We also describe techniques for efficient calculations of model's estimates, which lets a user exploit accuracy-complexity tradeoffs.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.959

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 head0.032
GPT teacher head0.269
Teacher spread0.238 · 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