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用于锂离子电池的低温电解质:当前挑战、发展与展望

Low-Temperature Electrolytes for Lithium-Ion Batteries: Current Challenges, Development, and Perspectives.

作者信息

Zhao Yang, Geng Limin, Meng Weijia, Ye Jiaye

机构信息

Xi'an Key Laboratory of Advanced Transport Power Machinery, School of Energy and Electrical Engineering, Chang'an University, Xi'an, 710064, People's Republic of China.

Shaanxi Key Laboratory of New Transportation Energy and Automotive Energy Saving, School of Energy and Electrical Engineering, Chang'an University, Xi'an, 710064, People's Republic of China.

出版信息

Nanomicro Lett. 2025 Sep 12;18(1):65. doi: 10.1007/s40820-025-01914-x.

Abstract

Lithium-ion batteries (LIBs), while dominant in energy storage due to high energy density and cycling stability, suffer from severe capacity decay, rate capability degradation, and lithium dendrite formation under low-temperature (LT) operation. Therefore, a more comprehensive and systematic understanding of LIB behavior at LT is urgently required. This review article comprehensively reviews recent advancements in electrolyte engineering strategies aimed at improving the low-temperature operational capabilities of LIBs. The study methodically examines critical performance-limiting mechanisms through fundamental analysis of four primary challenges: insufficient ionic conductivity under cryogenic conditions, kinetically hindered charge transfer processes, Li⁺ transport limitations across the solid-electrolyte interphase (SEI), and uncontrolled lithium dendrite growth. The work elaborates on innovative optimization approaches encompassing lithium salt molecular design with tailored dissociation characteristics, solvent matrix optimization through dielectric constant and viscosity regulation, interfacial engineering additives for constructing low-impedance SEI layers, and gel-polymer composite electrolyte systems. Notably, particular emphasis is placed on emerging machine learning-guided electrolyte formulation strategies that enable high-throughput virtual screening of constituent combinations and prediction of structure-property relationships. These artificial intelligence-assisted rational design frameworks demonstrate significant potential for accelerating the development of next-generation LT electrolytes by establishing quantitative composition-performance correlations through advanced data-driven methodologies.

摘要

锂离子电池(LIBs)虽然因其高能量密度和循环稳定性在能量存储领域占据主导地位,但在低温运行时会出现严重的容量衰减、倍率性能下降以及锂枝晶形成等问题。因此,迫切需要对低温下锂离子电池的行为有更全面、系统的了解。这篇综述文章全面回顾了旨在提高锂离子电池低温运行能力的电解质工程策略的最新进展。该研究通过对四个主要挑战进行基础分析,系统地考察了关键的性能限制机制:低温条件下离子电导率不足、动力学上受阻的电荷转移过程、锂离子在固体电解质界面(SEI)的传输限制以及不受控制的锂枝晶生长。这项工作阐述了创新的优化方法,包括具有定制解离特性的锂盐分子设计、通过调节介电常数和粘度进行溶剂基体优化、用于构建低阻抗SEI层的界面工程添加剂以及凝胶聚合物复合电解质体系。值得注意的是,特别强调了新兴的机器学习引导的电解质配方策略,该策略能够对成分组合进行高通量虚拟筛选并预测结构-性能关系。这些人工智能辅助的合理设计框架通过先进的数据驱动方法建立定量的组成-性能相关性,在加速下一代低温电解质的开发方面显示出巨大潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1d72/12432001/c805d8a0831e/40820_2025_1914_Fig1_HTML.jpg

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