The scaling of charging rate and cycle number of commercial batteries

• Published in: Acta mechanica Sinica Vol. 38; no. 5
• Main Authors: Wen, Jici, Zou, Qingrong, Zhang, Zehui, Shi, Jian, Wei, Yujie
• Format: Journal Article
• Language: English
• Published: Beijing The Chinese Society of Theoretical and Applied Mechanics; Institute of Mechanics, Chinese Academy of Sciences 01.05.2022; Springer Nature B.V
• Edition: English ed.
• Subjects: Algorithms; Batteries; Charging; Classical and Continuum Physics; Computational Intelligence; Cyclic loads; Engineering; Engineering Fluid Dynamics; Fatigue life; Life prediction; Machine learning; Optimization; Research Paper; S N diagrams; Scaling laws; Theoretical and Applied Mechanics; Electro-chemo-mechanical coupling; Cycle-life law; Charging rate; Lithium-ion battery; fatigue; -
• ISSN: 0567-7718; 1614-3116
• DOI: 10.1007/s10409-022-22108-x

Health management for commercial batteries is crowded with a variety of great issues, among which reliable cycle-life prediction tops. By identifying the cycle life of commercial batteries with different charging histories in fast-charging mode, we reveal that the average charging rate c and the resulted cycle life N of batteries obey c = c 0 N b , where c 0 is a limiting charging rate and b is an electrode-dependent constant. This c - N law, resembling the classic stress versus cycle number relationship (the S - N curve or Wohler curve) of solids subject to cyclic loading, could be applicable to most batteries. Such a scaling law, in combination with a physics-augmented machine-learning algorithm, could foster the predictability of battery life with high fidelity. The scaling of charging rate and cycle number may pave the way for cycle-life prediction and the directions of optimization of advanced batteries.