Feature selection strategy optimization for lithium-ion battery state of health estimation under impedance uncertainties

IF 13.1 1区 化学 Q1 Energy
Xinghao Du , Jinhao Meng , Yassine Amirat , Fei Gao , Mohamed Benbouzid
{"title":"Feature selection strategy optimization for lithium-ion battery state of health estimation under impedance uncertainties","authors":"Xinghao Du ,&nbsp;Jinhao Meng ,&nbsp;Yassine Amirat ,&nbsp;Fei Gao ,&nbsp;Mohamed Benbouzid","doi":"10.1016/j.jechem.2024.09.032","DOIUrl":null,"url":null,"abstract":"<div><div>Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles. Electrochemical impedance spectroscopy (EIS) offers valuable insights into battery degradation analysis and modeling. However, previous studies have not adequately addressed the impedance uncertainties, particularly during battery operating conditions, which can substantially impact the robustness and accuracy of state of health (SOH) estimation. Motivated by this, this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis. By utilizing metrics such as impedance residuals and correlation coefficients, the proposed method effectively filters out invalid and insignificant impedance data, thereby enhancing the reliability of the input features. Subsequently, the extreme gradient boosting (XGBoost) modeling framework is constructed for estimating the battery degradation trajectories. The XGBoost model incorporates a diverse range of hyperparameters, optimized by a genetic algorithm to improve its adaptability and generalization performance. Experimental validation confirms the effectiveness of the proposed feature optimization scheme, demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.</div></div>","PeriodicalId":15728,"journal":{"name":"Journal of Energy Chemistry","volume":"101 ","pages":"Pages 87-98"},"PeriodicalIF":13.1000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Energy Chemistry","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2095495624006569","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Energy","Score":null,"Total":0}
引用次数: 0

Abstract

Battery health evaluation and management are vital for the long-term reliability and optimal performance of lithium-ion batteries in electric vehicles. Electrochemical impedance spectroscopy (EIS) offers valuable insights into battery degradation analysis and modeling. However, previous studies have not adequately addressed the impedance uncertainties, particularly during battery operating conditions, which can substantially impact the robustness and accuracy of state of health (SOH) estimation. Motivated by this, this paper proposes a comprehensive feature optimization scheme that integrates impedance validity assessment with correlation analysis. By utilizing metrics such as impedance residuals and correlation coefficients, the proposed method effectively filters out invalid and insignificant impedance data, thereby enhancing the reliability of the input features. Subsequently, the extreme gradient boosting (XGBoost) modeling framework is constructed for estimating the battery degradation trajectories. The XGBoost model incorporates a diverse range of hyperparameters, optimized by a genetic algorithm to improve its adaptability and generalization performance. Experimental validation confirms the effectiveness of the proposed feature optimization scheme, demonstrating the superior estimation performance of the proposed method in comparison with four baseline techniques.

Abstract Image

阻抗不确定情况下锂离子电池健康状态估计的特征选择策略优化
电池健康评估和管理对于电动汽车中锂离子电池的长期可靠性和最佳性能至关重要。电化学阻抗光谱(EIS)为电池降解分析和建模提供了宝贵的见解。然而,以往的研究并未充分考虑阻抗的不确定性,尤其是在电池工作条件下,这会严重影响健康状况(SOH)估算的稳健性和准确性。受此启发,本文提出了一种将阻抗有效性评估与相关性分析相结合的综合特征优化方案。通过利用阻抗残差和相关系数等指标,本文提出的方法能有效过滤无效和不重要的阻抗数据,从而提高输入特征的可靠性。随后,构建了极端梯度提升(XGBoost)建模框架,用于估计电池退化轨迹。XGBoost 模型包含多种超参数,并通过遗传算法进行了优化,以提高其适应性和泛化性能。实验验证证实了所提出的特征优化方案的有效性,表明与四种基准技术相比,所提出的方法具有更优越的估算性能。
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
Journal of Energy Chemistry
Journal of Energy Chemistry CHEMISTRY, APPLIED-CHEMISTRY, PHYSICAL
CiteScore
19.10
自引率
8.40%
发文量
3631
审稿时长
15 days
期刊介绍: The Journal of Energy Chemistry, the official publication of Science Press and the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, serves as a platform for reporting creative research and innovative applications in energy chemistry. It mainly reports on creative researches and innovative applications of chemical conversions of fossil energy, carbon dioxide, electrochemical energy and hydrogen energy, as well as the conversions of biomass and solar energy related with chemical issues to promote academic exchanges in the field of energy chemistry and to accelerate the exploration, research and development of energy science and technologies. This journal focuses on original research papers covering various topics within energy chemistry worldwide, including: Optimized utilization of fossil energy Hydrogen energy Conversion and storage of electrochemical energy Capture, storage, and chemical conversion of carbon dioxide Materials and nanotechnologies for energy conversion and storage Chemistry in biomass conversion Chemistry in the utilization of solar energy
×
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