聚类分析
计算机科学
电池(电)
可靠性(半导体)
概化理论
降级(电信)
可靠性工程
鉴定(生物学)
电流(流体)
过程(计算)
数据挖掘
人工智能
功率(物理)
电气工程
工程类
操作系统
物理
统计
生物
电信
量子力学
植物
数学
作者
Zhiyu Zhou,Bo Lü,Yifei Qian,Xinsong Chen,Yicheng Song,Junqian Zhang
出处
期刊:Journal of The Electrochemical Society
[The Electrochemical Society]
日期:2023-12-01
卷期号:170 (12): 120533-120533
标识
DOI:10.1149/1945-7111/ad1554
摘要
Traditional evaluation of battery charging protocols typically requires hundreds of electrochemical cycles and months of experimentation to select charging schemes that maximize the battery performance without compromising the cycle life. In this work, by nesting clustering and classification algorithms, a data-driven method using only data within a few tens of cycles is proposed to accurately classify constant-current charging protocols and rapidly identify the critical current, beyond which rapid degradation tends to occur within a specified lifetime. Specifically, by utilizing unsupervised clustering to process early-stage features and generate prediction labels, a model for early-stage prediction of the rapid degradation is established with an accuracy higher than 92.75%. Subsequently, the critical current is determined by intersecting the classification boundary with the physical distribution domain of the features. The reliability and generalizability of the proposed method is also discussed, which suggests that only ∼30 cycles and ∼40 samples are required to accomplish acceptable identification. The method is also proven to suitable for different battery systems. Therefore, the data-driven method proposed in this work provides a novel pathway to rapidly evaluate fast-charging batteries and charging protocols.
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