A scenario-based genetic algorithm for controlling supercapacitor aging and degradation in the industry 4.0 era

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24220%2F24%3A00012107" target="_blank" >RIV/46747885:24220/24:00012107 - isvavai.cz</a>

Alternative codes found

RIV/46747885:24410/24:00012107 RIV/46747885:24620/24:00012107

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0952197624001738?dgcid=coauthor#ack0010" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0952197624001738?dgcid=coauthor#ack0010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.engappai.2024.108015" target="_blank" >10.1016/j.engappai.2024.108015</a>

Result language

angličtina

Original language name

A scenario-based genetic algorithm for controlling supercapacitor aging and degradation in the industry 4.0 era

Original language description

Electric double layer capacitors (EDLCs) are promising energy storage solutions, yet aging and degradation issues impede reliability and lifespan. The research proposes integrated simulation, modeling, and optimization to actively control EDLC degradation during charge-discharge cycles, mathematically modeling and simulating electrical and aging dynamics. These aging simulations are coupled with a genetic algorithm (GA) optimization routine that identifies the optimal combinations of influential EDLC parameters like internal resistance and capacitance for mitigating deterioration. Equivalent circuit models quantify electrical signatures, as aging factors induce gradual drifts in capacitance and resistance during thousands of simulated operational cycles. These MATLAB simulations effectively capture aging phenomena noted in real EDLCs in terms of measurable capacitance fading and resistance growth trends over continual usage in line with experimental data. The GA optimization subsequently determines optimal charging voltage ranges and achievable reductions in charge/discharge asymmetry by over 70 % that significantly enhance lifespan trajectories through aging control under similar test conditions. The technique‘s efficacy is further ascertained through systematic tuning of GA parameters like mutation rates and population sizes using Taguchi experimental models. The findings showcase superior optimization outcomes for larger populations and lower mutation probabilities. The research integrates digital twin for rapid evaluations, addressing reliability challenges via computational aging control. The flexible modeling platform enables customized what-if analyses for EDLC designers, aiding in material, cycling, and duty cycle exploration. This aging mitigation approach offers simulation-driven insights and automated optimization tools to extend the operational duration of high-performance EDLCs.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

<a href="/en/project/LM2023066" target="_blank" >LM2023066: Nanomaterials and Nanotechnologies for Environment Protection and Sustainable Future</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

—

Volume of the periodical

133

Issue of the periodical within the volume

July

Country of publishing house

GB - UNITED KINGDOM

Number of pages

18

Pages from-to

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UT code for WoS article

001207952200001

EID of the result in the Scopus database

2-s2.0-85186383831