Extremely Fast-Charging Batteries: Principle, Strategies, Detection, and Prediction

Extremely fast-charging (XFC) of lithium-ion batteries (LIBs) is critical for eliminating "charging anxiety" and accelerating the adoption of electric transportation, including electric vehicles and electric aircraft. However, two obstacles to achieving XFC in commercial LIBs are slow electrochemical kinetics and failure uncertainty, which lead to challenges such as limited capacity, rapid energy loss, and severe safety concerns under high-power charging. Therefore, a comprehensive overview of current research on XFC LIBs is essential to guide academia and industry in advancing XFC technology. This review examines the complex challenges, improvement strategies, issue detection, and advanced prediction methods related to XFC lithium-ion batteries. First, we analyze the physicochemical conflicts and key limitations affecting fast charging. Next, we discuss multiscale modulation strategies to enhance ion and electron transport. We also outline current detection and characterization techniques for diagnosing XFC failure mechanisms. To clarify safety boundaries, we explore multidimensional prediction methods for proactive risk identification. Finally, we highlight future research directions essential for further advancements in XFC technology.

Authors

Institutions

Publication Details

Journal
Chemical Reviews
Published
2025-10-02
DOI
https://doi.org/10.1021/acs.chemrev.5c00203
Citations
13
Primary Topic
Advanced Battery Technologies Research
Type
article
Field-Weighted Citation Impact
8.88
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Extremely Fast-Charging Batteries: Principle, Strategies, Detection, and Prediction

Yusheng Ye, Ruixing Li, Baoling Huang, Feng Wu et al.
13 citations
Chemical Reviews
Advanced Battery Technologies Research
8.88
article

Extremely Fast-Charging Batteries: Principle, Strategies, Detection, and Prediction

Yusheng Ye, Ruixing Li, Baoling Huang, Feng Wu, Xintao Yang, Li Li, Han Liu, Renjie Chen, Yongxin Zhang, Liyuan Zhao, Qianya Li, Hao Liu
article en
13 citations

Abstract

Extremely fast-charging (XFC) of lithium-ion batteries (LIBs) is critical for eliminating "charging anxiety" and accelerating the adoption of electric transportation, including electric vehicles and electric aircraft. However, two obstacles to achieving XFC in commercial LIBs are slow electrochemical kinetics and failure uncertainty, which lead to challenges such as limited capacity, rapid energy loss, and severe safety concerns under high-power charging. Therefore, a comprehensive overview of current research on XFC LIBs is essential to guide academia and industry in advancing XFC technology. This review examines the complex challenges, improvement strategies, issue detection, and advanced prediction methods related to XFC lithium-ion batteries. First, we analyze the physicochemical conflicts and key limitations affecting fast charging. Next, we discuss multiscale modulation strategies to enhance ion and electron transport. We also outline current detection and characterization techniques for diagnosing XFC failure mechanisms. To clarify safety boundaries, we explore multidimensional prediction methods for proactive risk identification. Finally, we highlight future research directions essential for further advancements in XFC technology.

Chemical ReviewsVol. 125(20)
Beijing Institute of Technology (CN), Advanced Energy (United States) (US), Division of Materials Science and Engineering (AU), Zhuhai Institute of Advanced Technology (CN)
Openalex Percentile: Top 2%
Advanced Battery Technologies Research
8.88
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.