A comprehensive understanding of multiple Li kinetics in batteries is essential to break the limitations of mechanism study and materials design. Various kinetic processes with specific relaxation features can be clearly identified in timescales. Extracting and analyzing the timescale information in batteries will provide insights into investigating kinetic issues such as ionic conductions, charge transfer, diffusions, interfacial evolutions, and other unknown kinetic processes. In this regard, the timescale identification is an important method to combine with the non-destructive impedance characterizations in length scale for online battery monitoring. This perspective introduces and advocates the timescale characterization in the views of the basic timescale property in batteries, employing the concept of distribution of relaxation time (DRT) and presenting successful applications for battery diagnosis. In the future, we suggest that timescale characterizations will become powerful tools for data extraction and dataset building for various battery systems, which can realize data-driven machine learning modeling for practical application situations such as retired battery rapid sorting and battery status estimations.

全面了解电池中的多种锂动力学对于打破机理研究和材料设计的局限性至关重要。可以在时间尺度上清楚地识别具有特定弛豫特征的各种动力学过程。提取和分析电池中的时间尺度信息将为研究离子传导、电荷转移、扩散、界面演化和其他未知动力学过程等动力学问题提供见解。在这方面，时间尺度识别是结合长度尺度的无损阻抗表征进行在线电池监测的重要方法。该观点在电池基本时间尺度特性的观点中引入并提倡时间尺度表征，采用弛豫时间 (DRT) 分布的概念，并展示了电池诊断的成功应用。未来，我们建议时间尺度表征将成为各种电池系统数据提取和数据集构建的强大工具，可以实现数据驱动的机器学习建模，用于退役电池快速分拣和电池状态估计等实际应用场景。