On full-life-cycle SOC estimation for lithium batteries by a variable structure based fractional-order extended state observer

内阻 控制理论(社会学) 电池(电) 观察员(物理) 荷电状态 计算机科学 功率(物理) 量子力学 物理 人工智能 控制(管理)
作者
Xu Zhao,Yongan Chen,Luo-jia Chen,Ning Chen,Hao Wang,Wei Huang,Jiayao Chen
出处
期刊:Applied Energy [Elsevier]
卷期号:351: 121828-121828 被引量:14
标识
DOI:10.1016/j.apenergy.2023.121828
摘要

Accurate SOC estimation of lithium batteries are crucial for the efficient operation of new energy storage systems. During the ageing of the battery, structure and parameters of the battery model, especially internal resistance, may change, which has a particularly significant impact on the accuracy of the model. For this reason, this paper proposes a SOC estimation method based on the extended state observer of the variable structure fractional order model. Firstly, an adaptive method for the structure and parameters of fractional order model through distribution of relaxation times (DRT) is proposed on full-cycle-life of lithium battery. The DRT is extracted from the Electrochemical Impedance Spectroscopy (EIS) of the lithium battery. The order and the initial parameters of the fractional order model of the lithium battery is determined by the characteristics of DRT during the ageing process of the lithium battery. Adaptive adjustment of model is realized by parameter identification combining with time domain data. Then, a fractional-order extended state observer is proposed to estimate SOC by treating internal resistance as an extended state, thus realizing online estimation of internal resistance uncertainty. The Lyapunov stability analysis proves that the estimation error of the observer is uniformly ultimately bounded. Finally, the experimental simulation analysis shows that the accuracy of the second-order model is significantly improved compared with the first-order model, and the accuracy improvement of the third-order model is limited compared with the second-order model. The MAE of the proposed algorithm is as low as 0.73%.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
完美世界应助生动紫青采纳,获得10
刚刚
忆年慧逝完成签到,获得积分10
1秒前
wx完成签到 ,获得积分10
1秒前
愉快豪发布了新的文献求助10
1秒前
美满的小懒虫完成签到 ,获得积分10
1秒前
科研波比发布了新的文献求助10
1秒前
1秒前
2秒前
CipherSage应助sunny采纳,获得10
2秒前
QMint完成签到,获得积分20
2秒前
科目三应助清新的梦桃采纳,获得10
2秒前
羞涩的文轩完成签到,获得积分10
2秒前
mayanting完成签到,获得积分20
3秒前
精明的远望完成签到,获得积分20
3秒前
肖肖完成签到,获得积分10
3秒前
SJJ应助jellorio采纳,获得30
3秒前
weisuonan101发布了新的文献求助10
3秒前
Hello应助小贺采纳,获得10
4秒前
风中傻姑发布了新的文献求助10
4秒前
崔哈哈完成签到,获得积分10
5秒前
田様应助KKKZ采纳,获得10
5秒前
5秒前
6秒前
6秒前
kxy0311发布了新的文献求助10
7秒前
夏夏完成签到,获得积分10
7秒前
laura完成签到,获得积分10
7秒前
phy发布了新的文献求助10
7秒前
8秒前
NexusExplorer应助有点懒采纳,获得10
8秒前
万能图书馆应助天天采纳,获得10
8秒前
完美世界应助肖肖采纳,获得10
8秒前
爆米花应助ananas42采纳,获得10
8秒前
健壮的涑完成签到 ,获得积分10
8秒前
积极的凝云完成签到,获得积分10
8秒前
胡房晓发布了新的文献求助10
9秒前
Marcus完成签到,获得积分10
9秒前
Hello应助精明的远望采纳,获得10
9秒前
冷傲疾应助务实文涛采纳,获得10
9秒前
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
First commercial application of ELCRES™ HTV150A film in Nichicon capacitors for AC-DC inverters: SABIC at PCIM Europe 1000
Feldspar inclusion dating of ceramics and burnt stones 1000
Digital and Social Media Marketing 600
Zeolites: From Fundamentals to Emerging Applications 600
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 5991780
求助须知:如何正确求助?哪些是违规求助? 7439810
关于积分的说明 16062902
捐赠科研通 5133395
什么是DOI,文献DOI怎么找? 2753529
邀请新用户注册赠送积分活动 1726334
关于科研通互助平台的介绍 1628329