Optimal Charging of Lithium-Ion Batteries: An Electro-Thermal Model Approach Using Maximum Possible Optimization

IF 2.9 4区 工程技术 Q1 MULTIDISCIPLINARY SCIENCES
Kamala Kumari Duru, Praneash Venkatachalam, Syed Ali Hussain, Asha Anish Madhavan, Sangaraju Sambasivam, Sujith Kalluri
{"title":"Optimal Charging of Lithium-Ion Batteries: An Electro-Thermal Model Approach Using Maximum Possible Optimization","authors":"Kamala Kumari Duru, Praneash Venkatachalam, Syed Ali Hussain, Asha Anish Madhavan, Sangaraju Sambasivam, Sujith Kalluri","doi":"10.1002/adts.202500320","DOIUrl":null,"url":null,"abstract":"Electric vehicle (EV) charging has recently become one of the most pressing issues. Given the growing demand for lithium-ion batteries (LIBs) in electric vehicles, this study analyzes optimization methods for improving existing approaches to speed up charging while reducing temperature rise. This work formulates a double-objective function for battery charging based on an electrothermal model. The focused objective function is comprised of a combination of two different fitness functions. Optimization of charging current is made dynamically following a battery's temperature. These experimental findings validate the proposed charging strategy's effectiveness in delivering the optimal current profile. This approach demonstrably achieves a well-calibrated balance between competing performance objectives. By adopting the suggested strategy, any increase in the battery's temperature can be maintained within an acceptable temperature range. The proposed constant current constant voltage (CCCV) charging method takes a total charging time of 1874 s, with a temperature shift from 26 to 45.78 <span data-altimg=\"/cms/asset/7fbfed2e-7e97-4aa8-acab-eecbe19f61f4/adts70005-math-0001.png\"></span><mjx-container ctxtmenu_counter=\"1\" ctxtmenu_oldtabindex=\"1\" jax=\"CHTML\" role=\"application\" sre-explorer- style=\"font-size: 103%; position: relative;\" tabindex=\"0\"><mjx-math aria-hidden=\"true\" location=\"graphic/adts70005-math-0001.png\"><mjx-semantics><mjx-mrow data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"2,3\" data-semantic-content=\"4\" data-semantic- data-semantic-role=\"implicit\" data-semantic-speech=\"Superscript ring Baseline normal upper C\" data-semantic-type=\"infixop\"><mjx-msup data-semantic-children=\"0,1\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"unknown\" data-semantic-type=\"superscript\"><mjx-mrow data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"empty\"></mjx-mrow><mjx-script style=\"vertical-align: 0.363em;\"><mjx-mo data-semantic- data-semantic-parent=\"2\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" size=\"s\"><mjx-c></mjx-c></mjx-mo></mjx-script></mjx-msup><mjx-mo data-semantic-added=\"true\" data-semantic- data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"5\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\" style=\"margin-left: 0.056em; margin-right: 0.056em;\"><mjx-c></mjx-c></mjx-mo><mjx-mi data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic- data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\"><mjx-c></mjx-c></mjx-mi></mjx-mrow></mjx-semantics></mjx-math><mjx-assistive-mml display=\"inline\" unselectable=\"on\"><math altimg=\"urn:x-wiley:25130390:media:adts70005:adts70005-math-0001\" display=\"inline\" location=\"graphic/adts70005-math-0001.png\" xmlns=\"http://www.w3.org/1998/Math/MathML\"><semantics><mrow data-semantic-=\"\" data-semantic-annotation=\"clearspeak:unit\" data-semantic-children=\"2,3\" data-semantic-content=\"4\" data-semantic-role=\"implicit\" data-semantic-speech=\"Superscript ring Baseline normal upper C\" data-semantic-type=\"infixop\"><msup data-semantic-=\"\" data-semantic-children=\"0,1\" data-semantic-parent=\"5\" data-semantic-role=\"unknown\" data-semantic-type=\"superscript\"><mrow data-semantic-=\"\" data-semantic-parent=\"2\" data-semantic-role=\"unknown\" data-semantic-type=\"empty\"></mrow><mo data-semantic-=\"\" data-semantic-parent=\"2\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\">∘</mo></msup><mo data-semantic-=\"\" data-semantic-added=\"true\" data-semantic-operator=\"infixop,⁢\" data-semantic-parent=\"5\" data-semantic-role=\"multiplication\" data-semantic-type=\"operator\">⁢</mo><mi data-semantic-=\"\" data-semantic-annotation=\"clearspeak:simple\" data-semantic-font=\"normal\" data-semantic-parent=\"5\" data-semantic-role=\"latinletter\" data-semantic-type=\"identifier\" mathvariant=\"normal\">C</mi></mrow>$^{\\circ }{\\rm C}$</annotation></semantics></math></mjx-assistive-mml></mjx-container>.","PeriodicalId":7219,"journal":{"name":"Advanced Theory and Simulations","volume":"230 1","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Theory and Simulations","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/adts.202500320","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Electric vehicle (EV) charging has recently become one of the most pressing issues. Given the growing demand for lithium-ion batteries (LIBs) in electric vehicles, this study analyzes optimization methods for improving existing approaches to speed up charging while reducing temperature rise. This work formulates a double-objective function for battery charging based on an electrothermal model. The focused objective function is comprised of a combination of two different fitness functions. Optimization of charging current is made dynamically following a battery's temperature. These experimental findings validate the proposed charging strategy's effectiveness in delivering the optimal current profile. This approach demonstrably achieves a well-calibrated balance between competing performance objectives. By adopting the suggested strategy, any increase in the battery's temperature can be maintained within an acceptable temperature range. The proposed constant current constant voltage (CCCV) charging method takes a total charging time of 1874 s, with a temperature shift from 26 to 45.78 C$^{\circ }{\rm C}$.

Abstract Image

锂离子电池的最佳充电:一种使用最大可能优化的电热模型方法
近年来,电动汽车充电问题已成为人们关注的热点问题之一。考虑到电动汽车对锂离子电池(LIBs)的需求日益增长,本研究分析了改进现有方法的优化方法,以加快充电速度,同时降低温度上升。本文建立了基于电热模型的电池充电双目标函数。聚焦目标函数由两个不同适应度函数组合而成。根据电池温度动态优化充电电流。这些实验结果验证了所提出的充电策略在提供最佳电流分布方面的有效性。这种方法显然在相互竞争的性能目标之间达到了校准良好的平衡。通过采用建议的策略,电池温度的任何升高都可以保持在可接受的温度范围内。所提出的恒流恒压(CCCV)充电方法总充电时间为1874 s,温度变化从26°减去C$^{\circ}{\rm C}$。
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
Advanced Theory and Simulations
Advanced Theory and Simulations Multidisciplinary-Multidisciplinary
CiteScore
5.50
自引率
3.00%
发文量
221
期刊介绍: Advanced Theory and Simulations is an interdisciplinary, international, English-language journal that publishes high-quality scientific results focusing on the development and application of theoretical methods, modeling and simulation approaches in all natural science and medicine areas, including: materials, chemistry, condensed matter physics engineering, energy life science, biology, medicine atmospheric/environmental science, climate science planetary science, astronomy, cosmology method development, numerical methods, statistics
×
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