基于 Matrix Profile 的锂离子电池在线拐点起始检测
Lithium-ion Battery Online Knee Onset Detection by Matrix Profile
AI总结：
提出基于 Matrix Profile 的锂离子电池在线拐点起始检测方法，利用放电电压周期时间动态识别拐点并分类电池，结合 SOH 估计模型实现 0.22% 低误差。
摘要：
锂离子电池在拐点起始前仅轻微退化，此后退化加速直至寿命终止（EOL）。拐点起始标志着加速退化率的开始，对于提供电池性能变化的早期预警至关重要。然而，目前关于在线拐点起始识别的文献有限。此外，使用易于采集的测量数据进行此类识别是理想的选择。为解决这些挑战，开发了一种利用放电数据内时间信息的在线拐点起始识别方法。首先，通过动态时间规整提取轻微退化阶段放电电压周期中嵌入的时间动态。其次，在子序列相似性搜索过程中，Matrix Profile 揭示异常。当新周期的时间动态超过控制限且 profile index 指示状态变化时，检测到拐点起始。最后，利用识别出的拐点起始，根据其与电池 EOL 周期的强相关性将电池分类为长程或短程类别。在电池分类支持和相同统计分布下获取的训练数据辅助下，提出的 SOH 估计模型实现了增强的估计结果，均方根误差低至 0.22%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
