范围(计算机科学)
智能电网
透视图(图形)
电池(电)
背景(考古学)
人气
计算机科学
储能
领域(数学)
系统工程
风险分析(工程)
数据科学
工程类
电气工程
业务
人工智能
量子力学
生物
心理学
数学
社会心理学
纯数学
程序设计语言
功率(物理)
古生物学
物理
作者
K. Liu,T. Wang,Xinyan Liu,Hong‐Jie Peng
标识
DOI:10.1002/batt.202300596
摘要
Abstract Along with the growing popularity of electric vehicles (EVs) and smart grids, rechargeable batteries are playing an increasingly important role in the field of energy storage. To ensure a safe and stable operation, it remains essential to estimate the states of batteries accurately and efficiently in advance. Herein, we provide a perspective on data‐driven online prognosis of rechargeable batteries, where its scope and superiorities are first introduced. Four promising application scenarios in real world including battery manufacture, EVs, smart grid, and battery recycling are then discussed, followed by the challenges that require further investigation. We anticipate this perspective to attract more interest to this field, to illuminate potential directions for future researches, and to broaden the context of data‐driven physical and engineering sciences.
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