Model-based predictive diagnostics for primary and secondary batteries

摘要

Batteries are an integral part of many operational environments and are critical backup systems for many power and computer networks. Failure of the battery would lead to loss of operation, reduced capability and downtime. Moreover, battery testing and maintenance can be a significant expense in these applications. A method to accurately assess the condition (state of charge), capacity (amp-hr) and remaining charge cycles (remaining ufeful life) of primary and secondary batteries would provide significant benefit. This model-based effort is foused on primary and secondary battery predictive diagnostics. Accurate modeling characterization requires electrochemical and thermal elements. Data from virtual (parameteric system information) and available sensors will be combined using the existing ARL Data Fusion Workbench. In particular, information from the data fusion feature vectors is processes to achieve inferences about the state of the system. The output of fusion feature vectors and any usage information available on the battery can be evaluated using hybrid automated reasoning schemes consisting of fuzzy logic and neural networkcomponents.