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Analysis of CT-CV Charging Technique for Lithium-ion and NCM 18650 Cells

2020· article· en· W3018035973 on OpenAlexaff
Vinicius Albanas Marcis, A. V. J. S. Praneeth, L.M. Patnaik, Sheldon S. Williamson

Bibliographic record

Venue2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020) · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsConstant currentBattery (electricity)Lithium (medication)VoltageMaterials scienceAutomotive engineeringNuclear engineeringManganeseController (irrigation)Lithium-ion batteryElectric vehicleConstant (computer programming)Electrical engineeringComputer scienceAnalytical Chemistry (journal)ChemistryEngineeringPhysicsThermodynamicsMetallurgyPower (physics)

Abstract

fetched live from OpenAlex

Battery Technology is ramping up these days with enormous boost in electric vehicle (EV) and hybrid electric vehicle (HEV) industry. One of the promising and crucial in batteries is to provide safe and quick charging algorithms. An extension of constant temperature and constant voltage (CT-CV) charging technique is studied in this paper, with a battery under test (BUT) of chemistry type Nickel Manganese Cobalt (NMC) 18650 lithium-ion cell. The analysis of the charging time, and surface temperature rise along with the PID controller gains used is performed at ambient temperature of 20°C. The performance and surface temperature analysis during charging and also discharge can be seen in this paper. In addition, the experimental setup is explained in detail with emphasis on the implemented CTCV algorithm. Also, the CT-CV charging technique implemented is compared to the standard constant current and constant voltage (CC-CV). By the end, the experimental results on tests performed with NMC cell is presented.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.830
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.257
Teacher spread0.240 · 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.

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

Citations7
Published2020
Admission routes1
Has abstractyes

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