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Record W4413125635 · doi:10.1109/tsg.2025.3596775

Optimal Ship-to-Grid Dispatch Considering Battery Thermal and Voltage Electrochemical-Thermal-Coupled Constraints

2025· article· en· W4413125635 on OpenAlexafffund
Chao Lei, Shuangqi Li, Yu Christine Chen

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMaritime Transport Emissions and Efficiency
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsVoltageBattery (electricity)ThermalGridAutomotive engineeringElectrical engineeringComputer scienceControl theory (sociology)EngineeringPower (physics)PhysicsMathematicsThermodynamics

Abstract

fetched live from OpenAlex

This letter proposes an optimal ship-to-grid (S2G) dispatch model of electric ships (ESs) coupling onshore and/or offshore wind energy conversion systems (WECS) with shore-to-ship and/or offshore floating charging stations so that the shipboard battery energy storage system (BESS) can help to smooth out variable WECS output. Thermal and voltage constraints of battery cells based on the electrochemical-thermal-coupled model enable fast charging to full capacity in the constant-current (CC) charging mode while remaining within battery operational limits. Case studies demonstrate that the proposed optimal S2G dispatch solution is physically realizable and fully satisfies the battery charging needs of ESs within thermal security constraints.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.217
Teacher spread0.210 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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