Batteries form a core component of many machines and are often times critical to the well being and functional capabilities of the overall system. Failure of a battery could lead to reduced performance, operational impairment and even catastrophic failure, especially in aerospace systems. An efficient method for battery monitoring would greatly improve the reliability of such systems.
- Investigate prognostic algorithms
- Provide framework for a variety of prognostic applications
- Improve state of the art of battery health management
- Demonstrate capability on hardware
- Funded through ARMD IVHM task 4.2 "Electrical Power Systems"
Questions to be answered
Can the current mission be completed?
Given the health of the battery, is there enough charge left for anticipated load profile
(within allowable uncertainty bounds)
Dominant metrics: state of charge (SOC), state of health (SOH)
Can future missions be completed?
Given the health of the battery, at what point can typical future missions not be met?
Dominant metrics: end of life (EOL), state of health (SOH)
Aeronautics: one or more defined flights with ancillary power demands
Space: Sequential unique or repeated tasks
Develop a model that makes a prediction of end-of-charge and end-of-life based on rapid state of health (SOH) assessment