Search

The safety, durability and power density of lithium‐ion batteries (LIBs) are currently inadequate to satisfy the continuously growing demand of the emerging battery markets. Rapid progress has been made from material engineering to system design, combining experimental results and simulations to enhance LIB performance. Limited by spatial and temporal resolution, state‐of‐the‐art advanced characterization techniques fail to fully reveal the complex multi‐scale degradation mechanism in LIBs. Strengthening interaction and iteration between characterization and modeling improves the understanding of reaction mechanisms as well as design and management of LIBs. Herein, a seed cyber hierarchy and interactional network framework is demonstrated to evaluate the overall lifecycle of LIBs. The typical examples of bridging the characterization techniques and modeling are discussed. The critical parameters extracted from multi‐scale characterization can serve as digital inputs for modeling. Furthermore, advanced computational techniques including cloud computing, big data, machine learning, and artificial intelligence can also promote the comprehensive understanding and precise control of the whole battery lifecycle. Digital twins techniques will be introduced enabling the real‐time monitoring and control of LIBs, autonomous computer‐assisted characterizations and intelligent manufacturing. It is anticipated that this work will provide a roadmap for further intensive research on developing high‐performance LIBs and intelligent battery management. [ABSTRACT FROM AUTHOR]

Characterization, digital twins, machine learning, simulation Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle (Adv. Keywords: characterization; digital twins; machine learning; simulation EN characterization digital twins machine learning simulation 1 1 1 09/05/22 20220901 NES 220901 B Lithium Batteries b In article number 2200889, Kai Yang, Shichun Yang and co-workers demonstrate the potential of cyber hierarchy and interactional network framework (CHAIN) to bridge multi-scale characterization and modeling technologies to evaluate lithium battery overall lifecycle. [Extracted from the article]