Prediction Methods and Data Fusion for Prognostics of Primary and Secondary Batteries
Abstract
A method to accurately assess the state-of-charge (SOC), state-of-health (SOH), and state-of-life (SOL) of electrochemical energy sources provides significant benefit to operational systems. The model-based effort described here is focused on predictive diagnostics for primary and secondary batteries. It can also be applied to other electrochemical energy sources, such as fuel cells. This method is based on accurate modeling of the transport mechanisms within the battery and requires carefully developed electrochemical and thermal models. New features are developed from these models and are used in conjunction with several traditional measured parameters to assess the condition of the battery. Data fusion of feature vectors is used to develop inferences about the state of the system. The resulting output and any usage 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 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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 05, 2001
- Accession Number
- ADP013490
Entities
People
- Amulya K. Garga
- Carl S. Byington
- James D. Kozlowski
- Matthew J. Watson
- Todd A. Hay
Organizations
- Pennsylvania State University