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Record W4413384417 · doi:10.1016/j.est.2025.118054

Ultracapacitor management system for IoT-based streetlight application

2025· article· en· W4413384417 on OpenAlexaff
Henar Mike O. Canilang, Wansu Lim

Bibliographic record

VenueJournal of Energy Storage · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric and Hybrid Vehicle Technologies
Canadian institutionsSimon Fraser University
FundersCommercializations Promotion Agency for R and D OutcomesMinistry of Science and ICT, South KoreaNational Research Foundation of Korea
KeywordsInternet of ThingsComputer scienceBusinessEmbedded system

Abstract

fetched live from OpenAlex

This paper proposes the highly efficient ultracapacitor management system (UCMS) for IoT-enabled, solar-powered streetlights. Existing battery-based storage solutions for streetlighting suffer from limited cycle life, slow recharge rates, and high maintenance overhead, motivating the need for a more durable and responsive approach. The proposed UCMS optimizes ultracapacitor (UC) charging and discharging cycles, improving streetlight efficiency and lifespan. The internet-of-things (IoT) integration allows for real-time monitoring and control. The system performance is evaluated through simulation, certification testing, and real-world deployment. Key features of hardware and software design include the ability to charge UCs at a minimum current of 0.5 A and a fault-tolerant design for enhanced reliability. The UCMS demonstrated monitoring accuracy of ≤0.2 V for voltage and ≤ 0.1 A for current, with successful wireless control and data acquisition. Design considerations and insights for broader UC-based energy storage applications are also discussed in this paper.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.962
Threshold uncertainty score0.330

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 head0.004
GPT teacher head0.194
Teacher spread0.190 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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