Measurements and modelling of the response of an ultrasonic pulse to a lithium-ion battery as a precursor for state of charge estimation

电池(电) 超声波传感器 荷电状态 电荷(物理) 电气工程 锂离子电池 信号(编程语言) 离子 电压 材料科学 锂(药物) 计算机科学 电子工程 工程类 声学 化学 物理 功率(物理) 有机化学 程序设计语言 内分泌学 医学 量子力学
作者
R.J. Copley,Denis Cumming,Yi Wu,R.S. Dwyer-Joyce
出处
期刊:Journal of energy storage [Elsevier]
卷期号:36: 102406-102406 被引量:52
标识
DOI:10.1016/j.est.2021.102406
摘要

Lithium-ion batteries change their internal state during cycles of charge and discharge. The state of charge of a lithium-ion battery varies during the charging cycle and depends on the internal structure of the components which may degrade with use. Estimation of the state of charge is commonly performed by battery management systems that rely on charge counting and cell voltage measurement. Determining the physical state of the battery components is challenging. Recently, the response of an ultrasonic pulse to a battery has been successfully correlated with both change in state of charge and state of health, the quality of the approach is now well established. This study assesses the qualities contained within an ultrasound signal response by investigating the behaviour of ultrasonic waves as they pass through the components in a layered battery structure, as those components change with battery charge. A model has been developed to understand the nature of the ultrasound response and the features that provide a particular characteristic. This is useful as two apparently identical batteries can produce very different ultrasonic responses. Detailed data analysis has been performed to find which combination of data comparisons provides the strongest correlation with state of charge and guides decisions about future use of battery monitoring using ultrasound. Finally, a smart peak selection method has been developed to ensure that regardless of the nature of the ultrasound response, state of charge measurements are optimised by ensuring the regions of signal with best battery charge correlation are identified. This can greatly help with the automation of the process in a sensor-based battery management system.

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
2秒前
2秒前
机灵曼荷完成签到,获得积分10
3秒前
ironyss发布了新的文献求助10
3秒前
yang发布了新的文献求助10
3秒前
吕雄涛完成签到,获得积分10
3秒前
量子星尘发布了新的文献求助10
4秒前
还没想好发布了新的文献求助10
4秒前
4秒前
淡定草丛发布了新的文献求助10
5秒前
6秒前
在水一方应助欢喜的无招采纳,获得10
6秒前
猪猪hero发布了新的文献求助10
6秒前
upandcoming发布了新的文献求助10
6秒前
7秒前
蜉蝣完成签到 ,获得积分10
7秒前
吕雄涛发布了新的文献求助10
8秒前
8秒前
卡卡卡发布了新的文献求助10
9秒前
xiaotangyuan发布了新的文献求助10
9秒前
田様应助Alin采纳,获得10
10秒前
张嘉芬完成签到,获得积分10
10秒前
朴实路人完成签到,获得积分10
11秒前
客厅狂欢发布了新的文献求助10
11秒前
我是哈哈超人完成签到,获得积分10
11秒前
11秒前
13秒前
小白发布了新的文献求助10
13秒前
14秒前
量子星尘发布了新的文献求助10
15秒前
火的信仰完成签到 ,获得积分10
15秒前
FashionBoy应助zzm采纳,获得10
16秒前
16秒前
积极的曼彤完成签到,获得积分10
16秒前
17秒前
17秒前
量子星尘发布了新的文献求助10
18秒前
18秒前
18秒前
upandcoming完成签到,获得积分0
18秒前
高分求助中
2025-2031全球及中国金刚石触媒粉行业研究及十五五规划分析报告 25000
(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
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5704813
求助须知:如何正确求助?哪些是违规求助? 5158878
关于积分的说明 15242939
捐赠科研通 4858662
什么是DOI,文献DOI怎么找? 2607392
邀请新用户注册赠送积分活动 1558393
关于科研通互助平台的介绍 1516137