DocumentCode
2897197
Title
Model-based predictive diagnostics for primary and secondary batteries
Author
Kozlowski, James D. ; Byington, Carl S. ; Garga, Amulya K. ; Watson, Matthew J. ; Hay, Todd A.
Author_Institution
Appl. Res. Lab., Pennsylvania State Univ., State Coll., PA, USA
fYear
2001
fDate
2001
Firstpage
251
Lastpage
256
Abstract
The model-based effort described here is focused on predictive diagnostics for primary and secondary batteries. However, this novel approach can also be applied to other electrochemical energy sources such as fuel cells. This method is based on accurate parametric modeling of the transport mechanisms within the battery. This system knowledge was used for the careful development of electrochemical and thermal models. These models have been used to extract new features to be used in conjunction with several traditional measured parameters to assess the condition of the battery. The resulting output and any usable information available about the battery is then evaluated using hybrid automated reasoning schemes consisting of neural network and decision theoretic methods. The focus of this paper is on the model identification and data fusion of the monitored and virtual sensor data. The methodology and analysis presented in this paper is applicable to mechanical systems where multiple sensor types are used for diagnostic assessment
Keywords
computerised monitoring; decision theory; electrochemical analysis; neural nets; power engineering computing; primary cells; secondary cells; sensor fusion; thermal analysis; data fusion; decision theoretic methods; diagnostic assessment; electrochemical energy sources; electrochemical models; fuel cells; hybrid automated reasoning schemes; model identification; model-based predictive diagnostics; multiple sensor types; neural network; parametric modeling; primary batteries; secondary batteries; thermal models; transport mechanisms; virtual sensor data; Battery charge measurement; Data mining; Feature extraction; Fuel cells; Mechanical sensors; Mechanical systems; Neural networks; Parametric statistics; Predictive models; Sensor fusion;
fLanguage
English
Publisher
ieee
Conference_Titel
Applications and Advances, 2001. The Sixteenth Annual Battery Conference on
Conference_Location
Long Beach, CA
ISSN
1089-8182
Print_ISBN
0-7803-6545-3
Type
conf
DOI
10.1109/BCAA.2001.905133
Filename
905133
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