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

Kód výsledku v 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>

Nalezeny alternativní kódy

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

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/LM2023066" target="_blank" >LM2023066: Nanomateriály a nanotechnologie pro ochranu životního prostředí a udržitelnou budoucnost</a><br>

Návaznosti

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

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Engineering Applications of Artificial Intelligence

ISSN

0952-1976

e-ISSN

—

Svazek periodika

133

Číslo periodika v rámci svazku

July

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

18

Strana od-do

—

Kód UT WoS článku

001207952200001

EID výsledku v databázi Scopus

2-s2.0-85186383831