Title :
Prognostics in Battery Health Management
Author :
Goebel, Kai ; Saha, Bhaskar ; Saxena, Abhinav ; Celaya, Jose R. ; Christophersen, Jon P.
fDate :
8/1/2008 12:00:00 AM
Abstract :
In this article, we examine prognostics and health management (PHM) issues using battery health management of Gen 2 cells, an 18650-size lithium-ion cell, as a test case. We will show where advanced regression, classification, and state estimation algorithms have an important role in the solution of the problem and in the data collection scheme for battery health management that we used for this case study.
Keywords :
battery management systems; condition monitoring; maintenance engineering; pattern classification; regression analysis; secondary cells; state estimation; 18650-size lithium-ion cell; Gen 2 cells; battery health management; classification; prognostics and health management; regression; state estimation algorithms; Battery charge measurement; Battery management systems; Battery powered vehicles; Circuit testing; Hybrid electric vehicles; Instruments; Laboratories; Life testing; NASA; Temperature;
Journal_Title :
Instrumentation & Measurement Magazine, IEEE
DOI :
10.1109/MIM.2008.4579269
