The timescale identification decoupling complicated kinetic processes in lithium batteries

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 trans-fer, 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 character-izations in length scale for online battery monitoring. This perspec-tive 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 suc-cessful 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 bat-tery status estimations.

Automated summary

Comprehensive understanding of multiple Li kinetics in batteries is crucial for studying mechanisms and designing materials. Time scale information in batteries can provide insights into various kinetic processes, such as ionic conductions, charge transfer, and diffusion. Time scale characterization, combined with non-destructive impedance characterization, can be a powerful tool for online battery monitoring.