Lithium-ion batteries (LiBs) degrade slightly until the knee onset, after which the deterioration accelerates to end of life (EOL). The knee onset, which marks the initiation of the accelerated degradation rate, is crucial in providing an early warning of the battery's performance changes. However, there is only limited literature on online knee onset identification. Furthermore, it is good to perform such identification using easily collected measurements. To solve these challenges, an online knee onset identification method is developed by exploiting the temporal information within the discharge data. First, the temporal dynamics embedded in the discharge voltage cycles from the slight degradation stage are extracted by the dynamic time warping. Second, the anomaly is exposed by Matrix Profile during subsequence similarity search. The knee onset is detected when the temporal dynamics of the new cycle exceed the control limit and the profile index indicates a change in regime. Finally, the identified knee onset is utilized to categorize the battery into long-range or short-range categories by its strong correlation with the battery's EOL cycles. With the support of the battery categorization and the training data acquired under the same statistic distribution, the proposed SOH estimation model achieves enhanced estimation results with a root mean squared error as low as 0.22%.
翻译:摘要:锂离子电池(LiBs)在轻微老化状态下会略微劣化,膝部起始后,劣化加速进入使用寿命(EOL)阶段。膝部起始标志着加速退化离终点更近了,因此膝部起始的识别对于提供电池性能变化的早期警告很关键。然而,在线膝部启动检测方面的文献资料有限。而且,最好使用易于收集的数据来进行此类识别。为了解决这些问题,通过利用放电数据内的时间信息,提出了一种在线膝部起始识别方法。首先,使用动态时间规整提取在轻微劣化阶段内嵌入在放电电压周期内的时间动态。其次,在子序列相似性搜索期间,使用矩阵剖面暴露异常。当新周期的时间动态超过控制极限,并且剖面指数表明制度发生变化时,检测到膝部起始。最后,将鉴定的膝部起始与电池的EOL周期之间的强相关性用于将电池分类为长程或短程类别。利用电池分类和在相同统计分布下获取的训练数据,获得所提出的SOH评估模型更好的评估结果,实现均方根误差低至0.22%。