健康状况
桥(图论)
电池(电)
计算机科学
锂(药物)
集合(抽象数据类型)
透视图(图形)
国家(计算机科学)
风险分析(工程)
系统工程
可靠性工程
工程类
人工智能
医学
物理
功率(物理)
量子力学
内科学
程序设计语言
内分泌学
算法
作者
Matthieu Dubarry,George Baure,David Anseán
出处
期刊:Journal of electrochemical energy conversion and storage
[ASME International]
日期:2019-10-01
卷期号:17 (4)
被引量:57
摘要
Abstract State-of-health (SOH) is an essential parameter for the proper functioning of large battery packs. A wide array of methodologies has been proposed in the literature to track state of health, but they often lack the proper validation that needed to be universally adaptable to large deployed systems. This is likely induced by the lack of knowledge bridge between scientists, who understand batteries, and engineers, who understand controls. In this work, we will attempt to bridge this gap by providing definitions, concepts, and tools to apply necessary material science knowledge to advanced battery management systems (BMS). We will address SOH determination and prediction, as well as BMS implementation and validation using the mechanistic framework developed around electrochemical voltage spectroscopies. Particular focus will be set on the onset and the prediction of the second stage of accelerating capacity loss that is commonly observed in commercial lithium-ion batteries.
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