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. Battery prognostics is focused on predicting the end-of-discharge (EOD) and end-of-life (EOL) of a given battery, based on its current condition and future expected usage.
- Develop computationally efficient battery models suitable for EOD and EOL prediction
- Provide a framework for battery prognostics that can be applied in multiple application domains
- Provide battery discharge and aging data to the scientific community
- Demonstrate capability on multiple hardware and experimental platforms
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