Bridging Multiscale Characterization Technologies and Digital Modeling to Evaluate Lithium Battery Full Lifecycle

摘要

The safety, durability and power density of lithium-ion batteries (LIBs) arecurrently inadequate to satisfy the continuously growing demand of theemerging battery markets. Rapid progress has been made from material engineeringto system design, combining experimental results and simulations toenhance LIB performance. Limited by spatial and temporal resolution, stateof-the-art advanced characterization techniques fail to fully reveal the complexmulti-scale degradation mechanism in LIBs. Strengthening interaction anditeration between characterization and modeling improves the understandingof reaction mechanisms as well as design and management of LIBs. Herein,a seed cyber hierarchy and interactional network framework is demonstratedto evaluate the overall lifecycle of LIBs. The typical examples of bridging thecharacterization techniques and modeling are discussed. The critical parametersextracted from multi-scale characterization can serve as digital inputsfor modeling. Furthermore, advanced computational techniques includingcloud computing, big data, machine learning, and artificial intelligence canalso promote the comprehensive understanding and precise control of thewhole battery lifecycle. Digital twins techniques will be introduced enablingthe real-time monitoring and control of LIBs, autonomous computer-assistedcharacterizations and intelligent manufacturing. It is anticipated that thiswork will provide a roadmap for further intensive research on developinghigh-performance LIBs and intelligent battery management.