Dataset Identification:
Resource Abstract:
- This paper presents a novel battery health management technology for the new generation of electric unmanned aerial vehicles
powered by long-life, high-density, scalable power sources. Current reliability based techniques are insufficient to manage
the use of such batteries when they are an active power source with frequently varying loads in uncertain environments. The
technique presented here encodes the basic electrochemical processes of a Lithium-polymer battery in an advanced Bayesian
inference framework to simultaneously track battery state-of-charge as well as tune the battery model to make accurate predictions
of remaining useful life. Results from ground tests with emulated flight profiles are presented with discussions on the use
of such prognostics results for decision making.
Citation
- Title Predicting Battery Life for Electric UAVs
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- revision Date
2013-04-26T14:20:42
Resource language:
[u'en-US']
Constraints on resource usage:
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- Constraints
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- Use limitation statement:
- public
point of contact
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publisher
- individual Name {u'hasEmail': u'mailto:miryam.strautkalns@nasa.gov', u'fn': u'Miryam Strautkalns'}
- organisation Name
{u'subOrganizationOf': {u'subOrganizationOf': {u'name': u'U.S. Government'}, u'name': u'National Aeronautics and Space Administration'},
u'name': u'Dashlink'}
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- Contact information
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- Address
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- electronic Mail Address
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Metadata data stamp:
2013-04-26T14:20:42
Metadata contact
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publisher
Metadata scope code
dataset
Metadata standard for this record:
ISO 19115:2003 - Geographic information - Metadata
standard version:
ISO 19115:2003
Metadata record identifier:
DASHLINK_712
Metadata record format is ISO19139 XML (MD_Metadata)