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首页> 外文期刊>Journal of Solid Mechanics and Materials Engineering >Development of Acoustic Emission Clustering Method to Detect Degradation of Lithium Ion Batteries
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Development of Acoustic Emission Clustering Method to Detect Degradation of Lithium Ion Batteries

机译:声发射聚类检测锂离子电池降解的研究进展

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Lithium ion batteries are widely used for electrical devices. However, their degradation causes serious accidents such as fires or explosions. In order to detect the degradation of a lithium ion battery during charging and discharging in real time, an acoustic emission (AE) technique was applied and a clustering method was developed to extract AE signals caused by battery degradation. In this study, highly oriented pyrolytic graphite (HOPG) and a lithium metal were used as working and counter electrodes, respectively, and a glass fiber sheet was used as a separator. An ethylene carbonate / diethyl carbonate (EC/DEC) solution, which was used as the electrolyte solution, was sealed in a metallic shell. Degradation of this type of battery mainly occurred by gas evolution and fracture or exfoliation of the graphite electrode. During the first charging and discharging cycle, 499 AE signals were detected. AE signals were clustered by a method that was developed based on the waveform polarity, power spectrum, and enveloped waveform. These features were first clustered by correlation coefficients. AE signals which the Euclidean distance of each feature was close were clustered into the same cluster. AE signals were clustered into 43 clusters by this method. In order to characterize clustered AE signals from a degrading battery, the clustered signals were compared to artificial AE signals created by simulating degradation behavior in the battery. Gas evolution on an electrode in the battery was simulated by electrolysis of water. AE signals due to graphite fracture were simulated by mechanically fracturing the graphite in a Vickers indentation test. As a result, AE signals characterizing gas evolution were detected continuously during the cycle. This result constituted gas evolution phenomena in the battery. Fracture-related AE signals due to graphite electrode exfoliation tended to occur when the lithium intercalation rate changed. Thus, fracture or exfoliation of graphite was attributed to the formation of a solid electrolyte interface (SEI) or volume expansion/contraction due to lithium intercalation, respectively. Other classes AE signals were also discussed, of which some were attributed to the distortion of graphite.
机译:锂离子电池广泛用于电气设备。但是,它们的降解会导致严重的事故,例如火灾或爆炸。为了实时检测锂离子电池在充电和放电期间的退化,应用了声发射(AE)技术,并开发了一种聚类方法来提取由电池退化引起的AE信号。在这项研究中,分别使用高取向的热解石墨(HOPG)和锂金属作为工作电极和对电极,并使用玻璃纤维片作为隔膜。将用作电解质溶液的碳酸亚乙酯/碳酸二乙酯(EC / DEC)溶液密封在金属壳中。这种类型的电池的降解主要是由于气体逸出以及石墨电极的破裂或剥落而发生的。在第一个充电和放电周期中,检测到499个AE信号。 AE信号通过基于波形极性,功率谱和包络波形开发的方法进行聚类。这些特征首先通过相关系数进行聚类。每个特征的欧几里得距离很近的AE信号被聚类到同一簇中。 AE信号通过这种方法被聚类为43个簇。为了表征来自退化电池的群集AE信号,将群集信号与通过模拟电池中的退化行为创建的人工AE信号进行比较。通过水的电解模拟电池中电极上的气体逸出。通过在维氏压痕测试中机械破碎石墨,模拟了由于石墨断裂而产生的AE信号。结果,在循环期间连续检测到表征气体逸出的AE信号。该结果构成电池中的气体逸出现象。当锂嵌入速率改变时,倾向于发生由于石墨电极剥落而引起的与断裂相关的AE信号。因此,石墨的断裂或剥落分别归因于固体电解质界面(SEI)的形成或由于锂嵌入引起的体积膨胀/收缩。还讨论了其他类别的AE信号,其中一些归因于石墨的变形。

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