Multi-objective optimization of lithium-ion battery designs considering the dilemma between energy density and rate capability

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xiao-Ying Ma , Wen-Ke Zhang , Ying Yin , Kailong Liu , Xiao-Guang Yang
{"title":"Multi-objective optimization of lithium-ion battery designs considering the dilemma between energy density and rate capability","authors":"Xiao-Ying Ma ,&nbsp;Wen-Ke Zhang ,&nbsp;Ying Yin ,&nbsp;Kailong Liu ,&nbsp;Xiao-Guang Yang","doi":"10.1016/j.egyai.2024.100416","DOIUrl":null,"url":null,"abstract":"<div><p>Electrified transportation requires batteries with high energy density and high-rate capability for both charging and discharging. Li-ion batteries (LiBs) face a dilemma: increasing areal capacity and reducing electrode porosity to boost energy density often reduces rate capability due to a longer and more tortuous ion transfer path. Tailoring cell design parameters to balance these metrics is essential but challenging. Here, we present a multi-objective optimization framework targeting energy density, fast charging, high-rate discharging, and lifespan simultaneously. Four cell parameters—cathode areal capacity, N-P ratio, cathode porosity, and anode porosity—along with operating temperature, are selected as design variables. A physics-based pseudo-2D model, validated against experimental data, generates data to train the surrogate model, which is combined with the NSGA-II algorithm for rapid optimization. Three different objective calculation methods are compared to identify the maximum sum of energy densities, lowest polarization, and most balanced performance, respectively. Cell design parameters are optimized at different temperatures using the most balanced optimization method. Results demonstrate that elevating cell operating temperature achieves high-rate capability while maintaining high energy density, mitigating the energy-power trade-off and broadening battery design parameter ranges.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100416"},"PeriodicalIF":9.6000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266654682400082X/pdfft?md5=d3ce0c5b9c8dc1128ba8b4e5cbafe72c&pid=1-s2.0-S266654682400082X-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and AI","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S266654682400082X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Electrified transportation requires batteries with high energy density and high-rate capability for both charging and discharging. Li-ion batteries (LiBs) face a dilemma: increasing areal capacity and reducing electrode porosity to boost energy density often reduces rate capability due to a longer and more tortuous ion transfer path. Tailoring cell design parameters to balance these metrics is essential but challenging. Here, we present a multi-objective optimization framework targeting energy density, fast charging, high-rate discharging, and lifespan simultaneously. Four cell parameters—cathode areal capacity, N-P ratio, cathode porosity, and anode porosity—along with operating temperature, are selected as design variables. A physics-based pseudo-2D model, validated against experimental data, generates data to train the surrogate model, which is combined with the NSGA-II algorithm for rapid optimization. Three different objective calculation methods are compared to identify the maximum sum of energy densities, lowest polarization, and most balanced performance, respectively. Cell design parameters are optimized at different temperatures using the most balanced optimization method. Results demonstrate that elevating cell operating temperature achieves high-rate capability while maintaining high energy density, mitigating the energy-power trade-off and broadening battery design parameter ranges.

Abstract Image

锂离子电池设计的多目标优化,考虑能量密度和速率能力之间的两难选择
电气化交通需要能量密度高、充电和放电速率高的电池。锂离子电池(LiBs)面临着一个两难的问题:为了提高能量密度而增加电池的面积容量和降低电极孔隙率,往往会因为更长、更曲折的离子传输路径而降低速率能力。调整电池设计参数以平衡这些指标至关重要,但也极具挑战性。在此,我们提出了一个多目标优化框架,同时针对能量密度、快速充电、高速放电和使用寿命。我们选择了四个电池参数--阴极等容量、N-P 比、阴极孔隙率和阳极孔隙率--以及工作温度作为设计变量。根据实验数据验证的基于物理的伪二维模型生成了训练代理模型的数据,该模型与 NSGA-II 算法相结合,实现了快速优化。比较了三种不同的目标计算方法,以分别确定最大能量密度总和、最低极化和最均衡的性能。使用最平衡的优化方法对不同温度下的电池设计参数进行了优化。结果表明,提高电池工作温度可在保持高能量密度的同时实现高倍率能力,从而缓解能量-功率权衡问题并拓宽电池设计参数范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信