journal article·2026-06-01Open access

Pushing the boundaries of high-performance Li-ion batteries: manufacturing for harsh environments

Zezhou Lin, Wei Yuan, Haitao Huang

Abstract

Abstract Lithium-ion batteries (LIBs), central to modern energy storage systems, are widely used in portable electronics, electric vehicles (EVs), and grid storage because of their high energy density and long cycle life. However, growing technological demands expose the limitations of current LIBs under extreme conditions, including performance degradation, reduced cycle life, and insufficient safety and reliability. Overcoming these challenges requires transformative manufacturing strategies to redesign batteries from the atomic scale to the macroscopic scale. Extreme manufacturing, utilizing unconventional processes and extreme environmental conditions, provides a new pathway for designing and producing next-generation high-performance LIBs. This perspective bridges the energy and manufacturing fields, outlining key challenges and future directions in this interdisciplinary domain. It defines core pathways through five interconnected themes: extreme working conditions, dimensional control, fabrication techniques, structural design, and performance optimization. Specifically, the perspective explores how extreme fabrication techniques realize material dimensional control to enhance interface properties and electrochemical performance, how electrode structural design enables stable operation at higher performance limits, and how intelligence-driven optimization processes vast amounts of data to build intelligent systems, accelerating material development, enabling smart production monitoring, and facilitating full-cycle battery health management. Through the lens of extreme manufacturing, this perspective provides a structured framework for developing next-generation LIBs.
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Research context

OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Machine Learning in Materials Science

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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