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
刚刚
幸福的雨完成签到,获得积分10
刚刚
穆思柔发布了新的文献求助10
刚刚
曾经我是不爱喝水完成签到,获得积分10
刚刚
香蕉曼寒关注了科研通微信公众号
刚刚
巧克李发布了新的文献求助10
1秒前
1秒前
liu发布了新的文献求助30
1秒前
曾经凡儿完成签到,获得积分20
2秒前
懒羊羊完成签到 ,获得积分10
2秒前
ning完成签到,获得积分20
2秒前
霂辰完成签到,获得积分10
3秒前
3秒前
4秒前
陈涛发布了新的文献求助10
4秒前
材料人一枚完成签到,获得积分10
4秒前
科研通AI6.1应助panghu采纳,获得10
4秒前
5秒前
6秒前
十里八乡发布了新的文献求助10
6秒前
万能图书馆应助Meng采纳,获得10
6秒前
Lucas应助好运接收集成器采纳,获得10
7秒前
整齐靖儿完成签到,获得积分20
7秒前
7秒前
7秒前
丁芍药发布了新的文献求助10
7秒前
hsk关闭了hsk文献求助
7秒前
7秒前
桐桐应助王饱饱采纳,获得10
7秒前
bear完成签到,获得积分0
7秒前
Tasia发布了新的文献求助10
7秒前
8秒前
syf完成签到,获得积分10
8秒前
xiaobo完成签到,获得积分10
8秒前
阿烨发布了新的文献求助10
9秒前
大意的珠完成签到,获得积分20
9秒前
小蘑菇应助喜悦的半青采纳,获得10
10秒前
10秒前
10秒前
11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
Handbook of pharmaceutical excipients, Ninth edition 5000
Aerospace Standards Index - 2026 ASIN2026 3000
Digital Twins of Advanced Materials Processing 2000
Polymorphism and polytypism in crystals 1000
Signals, Systems, and Signal Processing 610
Discrete-Time Signals and Systems 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 纳米技术 有机化学 物理 生物化学 化学工程 计算机科学 复合材料 内科学 催化作用 光电子学 物理化学 电极 冶金 遗传学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 6039374
求助须知:如何正确求助?哪些是违规求助? 7769039
关于积分的说明 16226209
捐赠科研通 5185346
什么是DOI,文献DOI怎么找? 2774958
邀请新用户注册赠送积分活动 1757774
关于科研通互助平台的介绍 1641908