商业化
接口(物质)
背景(考古学)
电化学储能
电池(电)
计算机科学
转化式学习
系统工程
纳米技术
能量密度
工程类
能量(信号处理)
风险分析(工程)
妥协
储能
组分(热力学)
材料科学
锂离子电池
高效能源利用
锂电池
锂(药物)
新能源
电池组
作者
Ebrahim Zohourvahid Karimi,Stefan Iglauer,Muhammad Rizwan Azhar
标识
DOI:10.1016/j.cis.2025.103686
摘要
Solid-state batteries (SSBs) represent a transformative advancement in energy storage, offering superior safety, higher energy density and extended cycle life compared to conventional lithium-ion batteries (LIBs). However, challenges related to interface engineering-particularly in ensuring stable electrochemical performance and preventing lithium dendrite formation-have hindered their widespread adoption and can compromise safety. Effective interface engineering is critical for mitigating interfacial resistance, enhancing mechanical stability and preventing thermal runaway, all of which are vital for improving battery reliability. The integration of artificial intelligence (AI) and machine learning (ML) in this context accelerates battery optimization by enabling predictive modelling of interfacial behaviour, material discovery and strategies to prevent failure. By addressing these fundamental challenges, interface engineering, alongside AI-driven innovations, can play a pivotal role in ensuring the safe, long-term operation of SSBs, providing the foundation for their commercialization in applications such as electric vehicles (EVs) and grid-scale energy storage.
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