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I-Corps: Cell Failure Analysis of Lithium-ion Batteries

I-Corps: Cell Failure Analysis of Lithium-ion Batteries
I-Corps:锂离子电池的电池失效分析
批准号:
1445355
负责人:
Jie Shen
金额:
$5.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2014
资助国家:
美国
项目状态:
已结题
起止时间:
2014-07-01 至 2015-11-30

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中文摘要
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英文摘要
The failure causes and mechanisms of lithium-ion (Li-ion) batteries are crucial in improving the reliability of batteries used in hybrid vehicles, military and aerospace applications. Failure analysis of Li-ion battery cells is a very challenging task that can be further complicated by the disturbance of failure modes by any destructive measurement methods. Conducting an accurate, non-destructive failure analysis is the desire of every Li-ion battery manufacturer, OEM, dealer, and end-customer. Destructive measurement methods, which ultimately render the battery inoperable, are currently employed as a means to determine root cause of failure. Non-destructive test methods, such as load analysis, currently provide little insight as to why or how a Li-ion battery is failing. The proposed technology offers the customer a repeatable, non-destructive methodology of digital measurement that is capable of qualifying or quantifying material damage at a multilevel fashion. The proposed innovation is a battery failure testing system for evaluating potential failure modes and identifying root causes of concern within Li-Ion batteries. This system consists of software and hardware. The software includes a series of computer simulation programs based on unique algorithms and the concept of applying material damage mechanism into computer simulation. The hardware is composed of a series of fixtures, working platforms, instruments, and computers. The digital measurement technology proposed in this project is a novel methodology with the following unique features: (1) quantification of material damage in a multilevel way, (2) effective reconstruction of material defects with consideration of surface roughness, (3) accurate de-noising for high precision metrology, and (4) multilevel finite element meshing for supporting multi-scale/multi-physics simulation based on digital measurement data.
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CAREER: Robustness, Active Learning, Sparsity, and Fairness in Classification
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