On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10251927" target="_blank" >RIV/61989100:27240/22:10251927 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/article/10.1007/s44196-022-00114-4" target="_blank" >https://link.springer.com/article/10.1007/s44196-022-00114-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s44196-022-00114-4" target="_blank" >10.1007/s44196-022-00114-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

Popis výsledku v původním jazyce

In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay&apos;s model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay&apos;s model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon&apos;s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.

Název v anglickém jazyce

On a Novel Hybrid Manta Ray Foraging Optimizer and Its Application on Parameters Estimation of Lithium-Ion Battery

Popis výsledku anglicky

In this paper, we propose a hybrid meta-heuristic algorithm called MRFO-PSO that hybridizes the Manta ray foraging optimization (MRFO) and particle swarm optimization (PSO) with the aim to balance the exploration and exploitation abilities. In the MRFO-PSO, the concept of velocity of the PSO is incorporated to guide the searching process of the MRFO, where the velocity is updated by the first best and the second-best solutions. By this integration, the balancing issue between the exploration phase and exploitation ability has been further improved. To illustrate the robustness and effectiveness of the MRFO-PSO, it is tested on 23 benchmark equations and it is applied to estimate the parameters of Tremblay&apos;s model with three different commercial lithium-ion batteries including the Samsung Cylindrical ICR18650-22 lithium-ion rechargeable battery, Tenergy 30209 prismatic cell, Ultralife UBBL03 (type LI-7) rechargeable battery. The study contribution exclusively utilizes hybrid machine learning-based tuning for Tremblay&apos;s model parameters to overcome the disadvantages of human-based tuning. In addition, the comparisons of the MRFO-PSO with six recent meta-heuristic methods are performed in terms of some statistical metrics and Wilcoxon&apos;s test-based non-parametric test. As a result, the conducted performance measures have confirmed the competitive results as well as the superiority of the proposed MRFO-PSO.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2022

Kód důvěrnosti údajů

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

Název periodika

International Journal of Computational Intelligence Systems

ISSN

1875-6891

e-ISSN

1875-6883

Svazek periodika

15

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

FR - Francouzská republika

Počet stran výsledku

22

Strana od-do

nestrankovano

Kód UT WoS článku

000838652500002

EID výsledku v databázi Scopus

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