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. Display omitted Characterizations from different scales are powerful to unravel the hidden mechanisms of working batteries. The timescale diagnosis is an emerging strategy to disassemble the battery “black box” into isolated kinetic information, which not only contributes to a non-destructive practical battery test but also helps to decouple and quantify Li kinetics in-time dimensions such as interfacial properties, ion transportation, and charge transfer processes. This paper introduces the basic scientific knowledge, protocols, applications, and outlooks on the rapidly developing timescale analyses in various battery systems, such as solid-state batteries, metal-S/O2 batteries, and metal-ion batteries. We hope the fresh viewpoint can help to popularize the timescale analyses in both academic study and industry applications. To comprehensively study the Li kinetics in the “black box” of batteries, timescale identification is indispensable to unravel the hidden information such as interfacial properties, ionic conduction, and charge transfer. The distribution of relaxation time (DRT) is an emerging powerful strategy to realize an accurate battery diagnosis in timescales avoiding subjective errors. DRT is an algorithm-supported solution, which is promising for application in various electrochemistry systems, data-driven analyses, and online monitoring in the battery industry.