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Electrochemistry-based Battery Modeling for Prognostics

Author
Daigle, Matthew J.
Published
October 14, 2013.
Physical Description
1 electronic document
Additional Creators
Kulkarni, Chetan Shrikant
Online Version

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Unclassified, Unlimited, Publicly available.
Free-to-read Unrestricted online access
Summary
Batteries are used in a wide variety of applications. In recent years, they have become popular as a source of power for electric vehicles such as cars, unmanned aerial vehicles, and commercial passenger aircraft. In such application domains, it becomes crucial to both monitor battery health and performance and to predict end of discharge (EOD) and end of useful life (EOL) events. To implement such technologies, it is crucial to understand how batteries work and to capture that knowledge in the form of models that can be used by monitoring, diagnosis, and prognosis algorithms. In this work, we develop electrochemistry-based models of lithium-ion batteries that capture the significant electrochemical processes, are computationally efficient, capture the effects of aging, and are of suitable accuracy for reliable EOD prediction in a variety of usage profiles. This paper reports on the progress of such a model, with results demonstrating the model validity and accurate EOD predictions.
Other Subject(s)
Collection
NASA Technical Reports Server (NTRS) Collection.
Note
Document ID: 20140009120.
ARC-E-DAA-TN11275.
Annual Conference of the Prognostics and Health Management Society 2013; 14-17 Oct. 2013; New Orleans, LA; United States.
Terms of Use and Reproduction
Copyright, Distribution as joint owner in the copyright.
View MARC record | catkey: 15427837