The timescale identification decoupling complicated kinetic processes in lithium batteries
Source abstract
Abstract
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. 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.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Advanced Battery Materials and Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (100+)
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