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 BV]
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
可爱的函函应助时安采纳,获得10
1秒前
心碎的黄焖鸡完成签到 ,获得积分10
1秒前
王艳发布了新的文献求助10
1秒前
2秒前
顾矜应助哈哈镜阿姐采纳,获得10
3秒前
4秒前
liputao完成签到 ,获得积分10
5秒前
5秒前
活泼的飞双完成签到,获得积分10
6秒前
无奈柚子发布了新的文献求助10
7秒前
YC发布了新的文献求助10
7秒前
7秒前
iu发布了新的文献求助10
9秒前
10秒前
11秒前
cai完成签到,获得积分10
12秒前
wj发布了新的文献求助10
12秒前
想要发文章完成签到 ,获得积分10
13秒前
源源发布了新的文献求助10
14秒前
Owen应助Henry采纳,获得10
14秒前
万能图书馆应助d甩甩采纳,获得10
16秒前
深情安青应助d甩甩采纳,获得10
16秒前
彭于晏应助d甩甩采纳,获得10
16秒前
asdlxz发布了新的文献求助10
16秒前
17秒前
刘泽民完成签到,获得积分10
17秒前
18秒前
KEYANKANG完成签到,获得积分10
19秒前
可爱的函函应助墨与白采纳,获得10
21秒前
Rika_Ran发布了新的文献求助10
22秒前
Tumumu完成签到,获得积分10
23秒前
大模型应助科研通管家采纳,获得10
24秒前
24秒前
24秒前
24秒前
小曲应助科研通管家采纳,获得10
24秒前
asdlxz完成签到,获得积分10
24秒前
25秒前
28秒前
Jsl完成签到,获得积分10
29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
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Picture this! Including first nations fiction picture books in school library collections 1500
Signals, Systems, and Signal Processing 610
Unlocking Chemical Thinking: Reimagining Chemistry Teaching and Learning 555
Photodetectors: From Ultraviolet to Infrared 500
Cancer Targets: Novel Therapies and Emerging Research Directions (Part 1) 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6359701
求助须知:如何正确求助?哪些是违规求助? 8173732
关于积分的说明 17215390
捐赠科研通 5414697
什么是DOI,文献DOI怎么找? 2865615
邀请新用户注册赠送积分活动 1842916
关于科研通互助平台的介绍 1691124