Exploration of Imbalanced Regression in state-of-health estimation of Lithium-ion batteries

IF 8.9 2区 工程技术 Q1 ENERGY & FUELS
Zhibin Zhao, Bingchen Liu, Fujin Wang, Shiyu Zheng, Qiuyu Yu, Zhi Zhai, Xuefeng Chen
{"title":"Exploration of Imbalanced Regression in state-of-health estimation of Lithium-ion batteries","authors":"Zhibin Zhao,&nbsp;Bingchen Liu,&nbsp;Fujin Wang,&nbsp;Shiyu Zheng,&nbsp;Qiuyu Yu,&nbsp;Zhi Zhai,&nbsp;Xuefeng Chen","doi":"10.1016/j.est.2024.114542","DOIUrl":null,"url":null,"abstract":"<div><div>The state of health (SOH) estimation for lithium-ion batteries based on deep learning (DL) has made great progress. However, due to different electrochemical compositions of lithium-ion batteries, different ways of conducting experiments and other factors, the degradation process of some batteries shows longer early degradation time and shorter later degradation time, resulting in a long-tailed distribution of degradation data. This leads to the problem of data imbalance in SOH estimation tasks, which affects the accuracy of SOH estimation. This article explores the long-tailed distribution phenomenon in the field of batteries and the corresponding imbalanced regression problem it brings to the estimation of battery SOH. In addition, a method for improving model performance is proposed. Specifically, we use a quadratic interpolation and standardization method to analyze the battery data to ensure the consistency of data features. By discretized analysis of continuous problems, the label distribution smoothing (LDS) method is applied to deep neural networks to analyze and solve this imbalanced regression problem. By convolution processing with the kernel function and label distribution, the weights corresponding to different labels are calculated, which improves the estimation accuracy. We conducted battery aging experiments and verified that the degradation data follows a long-tailed distribution. The effectiveness of the final method was validated on our experimental data and a publicly available dataset.</div></div>","PeriodicalId":15942,"journal":{"name":"Journal of energy storage","volume":"105 ","pages":""},"PeriodicalIF":8.9000,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of energy storage","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352152X24041288","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

The state of health (SOH) estimation for lithium-ion batteries based on deep learning (DL) has made great progress. However, due to different electrochemical compositions of lithium-ion batteries, different ways of conducting experiments and other factors, the degradation process of some batteries shows longer early degradation time and shorter later degradation time, resulting in a long-tailed distribution of degradation data. This leads to the problem of data imbalance in SOH estimation tasks, which affects the accuracy of SOH estimation. This article explores the long-tailed distribution phenomenon in the field of batteries and the corresponding imbalanced regression problem it brings to the estimation of battery SOH. In addition, a method for improving model performance is proposed. Specifically, we use a quadratic interpolation and standardization method to analyze the battery data to ensure the consistency of data features. By discretized analysis of continuous problems, the label distribution smoothing (LDS) method is applied to deep neural networks to analyze and solve this imbalanced regression problem. By convolution processing with the kernel function and label distribution, the weights corresponding to different labels are calculated, which improves the estimation accuracy. We conducted battery aging experiments and verified that the degradation data follows a long-tailed distribution. The effectiveness of the final method was validated on our experimental data and a publicly available dataset.
锂离子电池健康状况评估中的不平衡回归探索
基于深度学习(DL)的锂离子电池健康状况(SOH)估算取得了很大进展。然而,由于锂离子电池的电化学成分不同、实验方式不同等因素,部分电池的降解过程表现为早期降解时间较长,后期降解时间较短,导致降解数据呈长尾分布。这就导致了 SOH 估计任务中的数据不平衡问题,影响了 SOH 估计的准确性。本文探讨了电池领域的长尾分布现象及其给电池 SOH 估算带来的相应不平衡回归问题。此外,本文还提出了一种改善模型性能的方法。具体来说,我们采用二次插值和标准化方法来分析电池数据,以确保数据特征的一致性。通过对连续问题的离散化分析,将标签分布平滑(LDS)方法应用于深度神经网络,以分析和解决这一不平衡回归问题。通过对核函数和标签分布进行卷积处理,计算出不同标签对应的权重,从而提高了估计精度。我们进行了电池老化实验,验证了退化数据遵循长尾分布。最终方法的有效性在我们的实验数据和公开数据集上得到了验证。
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 storage
Journal of energy storage Energy-Renewable Energy, Sustainability and the Environment
CiteScore
11.80
自引率
24.50%
发文量
2262
审稿时长
69 days
期刊介绍: Journal of energy storage focusses on all aspects of energy storage, in particular systems integration, electric grid integration, modelling and analysis, novel energy storage technologies, sizing and management strategies, business models for operation of storage systems and energy storage developments worldwide.
×
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