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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

机译:超声波脉冲对锂离子电池响应的测量和建模作为充电估计状态的前体

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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.
机译:锂离子电池在充电和放电循环期间改变其内部状态。锂离子电池的充电状态在充电循环期间变化,并且取决于可以使用的部件的内部结构。估计充电状态通常是由依赖电荷计数和电池电压测量的电池管理系统进行的。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健康状态的变化和健康状态的变化相关,现在的方法质量已经很好地建立。本研究评估超声波信号响应内容通过研究超声波的行为在分层电池结构中的组件中来评估超声波信号响应的质量,因为这些组件随电池电量而变化。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。这是有用的两个明显相同的电池可以产生非常不同的超声回答。已经执行了详细的数据分析,以查找数据比较的哪些组合提供了与充电状态最强的相关性,并指导有关使用超声波的未来使用电池监控的决策。最后,已经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,通过确保识别最佳电池充电的信号区域来优化充电状态。这可以极大地帮助基于传感器的电池管理系统中的过程的自动化。

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