Data–Knowledge-Dual-Driven Electrolyte Design for Fast-Charging Lithium Ion Batteries

Yi Yang, Nan Yao, Yu Chen Gao, Xiang Chen*, Yu Xin Huang, Shuo Zhang, Han Bing Zhu, Lei Xu, Yu Xing Yao, Shi Jie Yang, Zheng Liao, Zeheng Li, Xue Fei Wen, Peng Wu, Ting Lu Song, Jin Hao Yao, Jiang Kui Hu, Chong Yan, Jia Qi Huang*, Qiang Zhang*

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

9 引用 (Scopus)

摘要

Electric vehicles (EVs) starve for minutes-level fast-charging lithium-ion batteries (LIBs), while the heat gathering at high-rate charging and torridity conditions has detrimental effects on electrolytes, triggering rapid battery degradation and even safety hazards. However, the current research on high-temperature fast-charging (HTFC) electrolytes is very lacking. We revolutionized the conventional paradigm of developing HTFC electrolytes integrating with high-throughput calculation, machine-learning techniques, and experimental verifications to establish a data–knowledge-dual-driven approach. Ethyl trimethylacetate was efficiently screened out based on the approach and enabled batteries to work under high temperatures with distinctly restricted side reactions. A stable and highly safe fast-charging (15-min charging to 80% capacity) cycling without Li plating was achieved over 4100 cycles at 45 °C based on 181 Wh kg−1 pouch cells, demonstrating the state-of-the-art in this field.

源语言英语
文章编号e202505212
期刊Angewandte Chemie - International Edition
64
24
DOI
出版状态已出版 - 10 6月 2025
已对外发布

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