Electrode design optimization of lithium secondary batteries to enhance adhesion and deformation capabilities

Dongho Jeong, Jongsoo Lee

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Safety, performance and lifetime of LSB (lithium secondary batteries) are affected by the adhesion of the active material to the electrode substance, and to the electrode deformation and the spring back limit in the electrode manufacturing process. This study explores the optimization process using decision tree analysis, an ANN (artificial neural network), and a multi-objective genetic algorithm. In the electrode design optimization, the objectives are to maximize the adhesion and to minimize the electrode deformation subjected to the allowable limit on the spring-back. Experimental data for use in design analysis and optimization is obtained via a measurement test. The decision tree analysis is first performed to extract major, effective parameters sensitive to adhesion force, electrode deformation and spring-back. The ANN-based approximate meta-models are then established for function approximations. The ANN-based causality analysis is further explored to determine dominant design variables for each of three design requirements for the optimization. A multi-objective optimization is finally conducted using ANN-based approximate meta-models. An optimized solution obtained from the numerical optimization process is compared with experimental data to verify the actual performance of the LSB in terms of physical and electro-chemical properties.

Original languageEnglish
Pages (from-to)525-533
Number of pages9
JournalEnergy
Volume75
DOIs
Publication statusPublished - 2014 Oct 1

Fingerprint

Secondary batteries
Lithium
Adhesion
Electrodes
Neural networks
Decision trees
Multiobjective optimization
Electrochemical properties
Design optimization
Genetic algorithms

All Science Journal Classification (ASJC) codes

  • Pollution
  • Energy(all)

Cite this

@article{bdc635dc917d4781ad259ec55f736128,
title = "Electrode design optimization of lithium secondary batteries to enhance adhesion and deformation capabilities",
abstract = "Safety, performance and lifetime of LSB (lithium secondary batteries) are affected by the adhesion of the active material to the electrode substance, and to the electrode deformation and the spring back limit in the electrode manufacturing process. This study explores the optimization process using decision tree analysis, an ANN (artificial neural network), and a multi-objective genetic algorithm. In the electrode design optimization, the objectives are to maximize the adhesion and to minimize the electrode deformation subjected to the allowable limit on the spring-back. Experimental data for use in design analysis and optimization is obtained via a measurement test. The decision tree analysis is first performed to extract major, effective parameters sensitive to adhesion force, electrode deformation and spring-back. The ANN-based approximate meta-models are then established for function approximations. The ANN-based causality analysis is further explored to determine dominant design variables for each of three design requirements for the optimization. A multi-objective optimization is finally conducted using ANN-based approximate meta-models. An optimized solution obtained from the numerical optimization process is compared with experimental data to verify the actual performance of the LSB in terms of physical and electro-chemical properties.",
author = "Dongho Jeong and Jongsoo Lee",
year = "2014",
month = "10",
day = "1",
doi = "10.1016/j.energy.2014.08.013",
language = "English",
volume = "75",
pages = "525--533",
journal = "Energy",
issn = "0360-5442",
publisher = "Elsevier Limited",

}

Electrode design optimization of lithium secondary batteries to enhance adhesion and deformation capabilities. / Jeong, Dongho; Lee, Jongsoo.

In: Energy, Vol. 75, 01.10.2014, p. 525-533.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Electrode design optimization of lithium secondary batteries to enhance adhesion and deformation capabilities

AU - Jeong, Dongho

AU - Lee, Jongsoo

PY - 2014/10/1

Y1 - 2014/10/1

N2 - Safety, performance and lifetime of LSB (lithium secondary batteries) are affected by the adhesion of the active material to the electrode substance, and to the electrode deformation and the spring back limit in the electrode manufacturing process. This study explores the optimization process using decision tree analysis, an ANN (artificial neural network), and a multi-objective genetic algorithm. In the electrode design optimization, the objectives are to maximize the adhesion and to minimize the electrode deformation subjected to the allowable limit on the spring-back. Experimental data for use in design analysis and optimization is obtained via a measurement test. The decision tree analysis is first performed to extract major, effective parameters sensitive to adhesion force, electrode deformation and spring-back. The ANN-based approximate meta-models are then established for function approximations. The ANN-based causality analysis is further explored to determine dominant design variables for each of three design requirements for the optimization. A multi-objective optimization is finally conducted using ANN-based approximate meta-models. An optimized solution obtained from the numerical optimization process is compared with experimental data to verify the actual performance of the LSB in terms of physical and electro-chemical properties.

AB - Safety, performance and lifetime of LSB (lithium secondary batteries) are affected by the adhesion of the active material to the electrode substance, and to the electrode deformation and the spring back limit in the electrode manufacturing process. This study explores the optimization process using decision tree analysis, an ANN (artificial neural network), and a multi-objective genetic algorithm. In the electrode design optimization, the objectives are to maximize the adhesion and to minimize the electrode deformation subjected to the allowable limit on the spring-back. Experimental data for use in design analysis and optimization is obtained via a measurement test. The decision tree analysis is first performed to extract major, effective parameters sensitive to adhesion force, electrode deformation and spring-back. The ANN-based approximate meta-models are then established for function approximations. The ANN-based causality analysis is further explored to determine dominant design variables for each of three design requirements for the optimization. A multi-objective optimization is finally conducted using ANN-based approximate meta-models. An optimized solution obtained from the numerical optimization process is compared with experimental data to verify the actual performance of the LSB in terms of physical and electro-chemical properties.

UR - http://www.scopus.com/inward/record.url?scp=84908052806&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84908052806&partnerID=8YFLogxK

U2 - 10.1016/j.energy.2014.08.013

DO - 10.1016/j.energy.2014.08.013

M3 - Article

AN - SCOPUS:84908052806

VL - 75

SP - 525

EP - 533

JO - Energy

JF - Energy

SN - 0360-5442

ER -