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Record W2965929649 · doi:10.1109/isie.2019.8781156

Control Strategy for a Battery Energy Storage System with Parallel Paths

2019· article· en· W2965929649 on OpenAlexaff
Xiaoquan Xu, Shaahin Filizadeh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBattery (electricity)Energy storageComputer scienceConvertersControl (management)Power (physics)Controller (irrigation)GridComputer data storageEnergy (signal processing)Stability (learning theory)Control engineeringVoltageEngineeringElectrical engineeringComputer hardware

Abstract

fetched live from OpenAlex

Parallel connection of batteries using isolated dc-dc converters can increase the capacity of an energy storage system. It also allows usage of batteries with different chemistries and at various states of health. To achieve this, important questions with regard to the operation of batteries of different states of health, and system stability must be answered. This paper proposes a new control strategy for assignment of power references to batteries in a parallel-connected energy storage system. The proposed controller allocates power to each battery according to its present states of charge and health and reduces the stress on the aged batteries. The proposed control strategy is particularly useful in energy storage systems based upon re-purposed vehicular batteries, which are integrated into the grid at different states of health. The paper verifies the effective of the control strategy through detailed computer simulations.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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
Published2019
Admission routes1
Has abstractyes

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