Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments
Abstract The demand for high-capacity lithium-ion batteries (LIB) in electric vehicles has increased. In this study, optimization to maximize the specific energy density of a cell is conducted using the LIB electrochemical model and sequential approximate optimization (SAO). First, the design of exp...
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2020-09-01
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doaj-7e3593a1190042d4a804485dec338ace2021-09-26T11:25:15ZengNature Publishing GroupScientific Reports2045-23222020-09-0110111110.1038/s41598-020-72442-4Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experimentsJi-San Kim0Dong-Chan Lee1Jeong-Joo Lee2Chang-Wan Kim3Graduate School of Mechanical Design and Production Engineering, Konkuk UniversitySchool of Mechanical Engineering, Konkuk UniversityGraduate School of Mechanical Design and Production Engineering, Konkuk UniversitySchool of Mechanical Engineering, Konkuk UniversityAbstract The demand for high-capacity lithium-ion batteries (LIB) in electric vehicles has increased. In this study, optimization to maximize the specific energy density of a cell is conducted using the LIB electrochemical model and sequential approximate optimization (SAO). First, the design of experiments is performed to analyze the sensitivity of design factors important to the specific energy density, such as electrode and separator thicknesses, porosity, and particle size. Then, the design variables of the cell are optimized for maximum specific energy density using the progressive quadratic response surface method (PQRSM), which is one of the SAO techniques. As a result of optimization, the thickness ratio of the electrode was optimized and the porosity was reduced to keep the specific energy density high, while still maintaining the specific power density performance. This led to an increase in the specific energy density of 56.8% and a reduction in the polarization phenomenon of 11.5%. The specific energy density effectively improved through minimum computation despite the nonlinearity of the electrochemical model in PQRSM optimization.https://doi.org/10.1038/s41598-020-72442-4 |
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DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ji-San Kim Dong-Chan Lee Jeong-Joo Lee Chang-Wan Kim |
spellingShingle |
Ji-San Kim Dong-Chan Lee Jeong-Joo Lee Chang-Wan Kim Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments Scientific Reports |
author_facet |
Ji-San Kim Dong-Chan Lee Jeong-Joo Lee Chang-Wan Kim |
author_sort |
Ji-San Kim |
title |
Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments |
title_short |
Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments |
title_full |
Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments |
title_fullStr |
Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments |
title_full_unstemmed |
Optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments |
title_sort |
optimization for maximum specific energy density of a lithium-ion battery using progressive quadratic response surface method and design of experiments |
publisher |
Nature Publishing Group |
series |
Scientific Reports |
issn |
2045-2322 |
publishDate |
2020-09-01 |
description |
Abstract The demand for high-capacity lithium-ion batteries (LIB) in electric vehicles has increased. In this study, optimization to maximize the specific energy density of a cell is conducted using the LIB electrochemical model and sequential approximate optimization (SAO). First, the design of experiments is performed to analyze the sensitivity of design factors important to the specific energy density, such as electrode and separator thicknesses, porosity, and particle size. Then, the design variables of the cell are optimized for maximum specific energy density using the progressive quadratic response surface method (PQRSM), which is one of the SAO techniques. As a result of optimization, the thickness ratio of the electrode was optimized and the porosity was reduced to keep the specific energy density high, while still maintaining the specific power density performance. This led to an increase in the specific energy density of 56.8% and a reduction in the polarization phenomenon of 11.5%. The specific energy density effectively improved through minimum computation despite the nonlinearity of the electrochemical model in PQRSM optimization. |
url |
https://doi.org/10.1038/s41598-020-72442-4 |
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