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Record W4366982208 · doi:10.1155/2023/4554582

State-Flow Control Based Multistage Constant-Current Battery Charger for Electric Two-Wheeler

2023· article· en· W4366982208 on OpenAlexvenueno aff
P. Balamurugan, Prakhar Agarwal, Devashish Khajuria, Devbrat Mahapatra, S. Angalaeswari, Wubishet Degife Mammo

Bibliographic record

VenueJournal of Advanced Transportation · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
FundersVIT UniversitySaveetha Institute of Medical and Technical Sciences
KeywordsTotal harmonic distortionBattery (electricity)Constant currentState of chargeMATLABController (irrigation)Control theory (sociology)Current limitingEngineeringElectric vehicleVoltageBattery chargerComputer sciencePower (physics)Automotive engineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

Battery charging is a greater challenge in the emerging electric vehicle domain. A newer multistage constant-current (MSCC) charging technique encompassing state-flow control tool-based design is implemented for charging the battery of an electric two-wheeler. MSCC method allows for faster charging and reduced battery degradation per charge. The designed controller incorporates line current power factor correction, thereby limiting the total harmonic distortion (THD) in line current and reactive power. The control strategy for battery charging has been developed using the state flow chart approach for implementing MSCC. The model has been formulated and implemented in MATLAB/Simulink. The proposed control monitors the state-of-charge (SOC) of the battery, age, and thermal behavior due to the charging strategy. The results show that the proposed charging technique with a state flow control approach gives effective and efficient output with reduced THD. Simulation results disclose that the desired parameters are controllable, stable, and effective within the operational limits.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.648
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.014
GPT teacher head0.283
Teacher spread0.269 · 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 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

Citations33
Published2023
Admission routes1
Has abstractyes

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