Measurements and modelling of the response of an ultrasonic pulse to a lithium-ion battery as a precursor for state of charge estimation
Measurements and modelling of the response of an ultrasonic pulse to a lithium-ion battery as a precursor for state of charge estimation
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DOI：
10.1016/j.est.2021.102406
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发表时间：
2021-03-03
影响因子：
9.4
通讯作者：
Dwyer-Joyce, R. S.
中科院分区：
文献类型：
--
作者：
Copley, R. J.;Cumming, D.;Dwyer-Joyce, R. S.
锂离子电池在充放电循环过程中会改变其内部状态。锂离子电池的充电状态在充电周期中有所不同，并取决于组件的内部结构，这些组件可能会随着使用而退化。充电状态的估计通常由依赖于电荷计数和电池电压测量的电池管理系统来执行。确定电池组件的物理状态是具有挑战性的。最近，超声波脉冲对电池的响应已经成功地与电荷状态和健康状态的变化相关联，这种方法的质量现在已经得到了很好的证实。这项研究通过调查超声波通过分层电池结构中的组件时的行为，来评估超声波信号响应中包含的质量，因为这些组件随着电池电量的变化而变化。已经开发了一种模型来了解超声响应的性质和提供特定特征的特征。这很有用，因为两个表面上相同的电池可以产生非常不同的超声波响应。已经进行了详细的数据分析，以找出哪种数据比较组合提供了与充电状态最强的相关性，并指导了未来使用超声波监测电池的决定。最后，开发了一种智能峰值选择方法，以确保无论超声响应的性质如何，都可以通过确保识别具有最佳电池电荷相关性的信号区域来优化荷电状态测量。这可以极大地帮助基于传感器的电池管理系统中的过程自动化。
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.