预言
电池(电)
健康状况
计算机科学
汽车工程
可靠性工程
云计算
电池容量
大数据
状态监测
工程类
系统工程
电气工程
数据挖掘
量子力学
功率(物理)
物理
操作系统
作者
Jingyuan Zhao,Nan Jiang,Junbin Wang,Heping Ling,Yubo Lian,Andrew Burke
标识
DOI:10.1109/vppc55846.2022.10003378
摘要
Expending manufacturing capacity and development of high-energy batteries greatly stimulate the growth and applications of electric vehicles (EVs). However, battery diagnostics and prognostics related to capacity degradation (referred as state of health, SOH) and safety issues (referred as state of safety, SOS) in real-world applications is still a big deal. Due to the uncertainties in materials and manufacturing, dynamic operation conditions as well as a lack of plentiful, high-quality on-road data, accurate diagnosis of battery performance for “real EVs” is very challenging. Considering the difficulty in accurately predicting battery behaviors in real-world applications, brand-new control area networks (CAN) and cloud-based solution could have considerable benefits. An AI-powered cloud-based framework integrating longitudinal electronic health records with real-world data enables continuous battery performance evaluation for EVs. This offers opportunities for combining data generation with data-driven approaches to predict the behavior of complex, time-varying electrochemical systems. It is hoped that this paper will be of reference value to the EV and battery industries for ameliorating some of the hurdles for battery diagnostics and prognostics under realistic EV conditions.
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