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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.

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