Model-based predictive diagnostics for electrochemical energy sources

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 mechanism...

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Bibliographic Details
Main Authors: Kozlowski, J.D., Byington, C.S., Garga, A.K., Watson, M.J., Hay, T.A.
Format: Conference Proceeding
Language:English
Subjects:
Battery charge measurement
Data mining
Feature extraction
Fuel cells
Mechanical sensors
Mechanical systems
Neural networks
Parametric statistics
Predictive models
Sensor fusion
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Description
Summary: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 is applicable to mechanical systems where multiple sensor types are used for diagnostic assessment.
ISSN:1095-323X
DOI:10.1109/AERO.2001.931332