The objective of this research is to develop prognostic methods for improving the safety and availability of battery-powered systems such as electric vehicles and unmanned aerial vehicles. Battery-powered systems suffer from two problems. The first is "range anxiety" problem, which refers to the fear of running out of battery power during vehicle operation. The second problem is related to the safety of a battery pack, which can rupture or even explode under certain conditions. To address these problems, an approach for predicting the (1) end-of-discharge (the time at which a battery will run out of electrical charge), and (2) remaining-useful-performance of a battery with a known level of confidence will be developed. This approach involved machine learning algorithms to address future loading conditions, unit-to-unit variations, and modeling uncertainties.
This research will improve the operation readiness and safety of battery-powered systems that are used in applications ranging from commercial (electric vehicles) to defense (unmanned aerial vehicles) sectors. In 2011, President Obama announced his goal of having one million electric vehicles on the road by 2015. The proposed research will make significant contributions to reaching this target, since it can ease user concern about the safety and reliability of electric vehicles by providing robust battery state and health information in real-time, thus encouraging their widespread use. As a result, it will also decrease US dependence on foreign oil and reduce the emission of greenhouse gases. The content of this research will assist in the advancement of prognostics and health management techniques which will be disseminated to the engineering community through online graduate courses, seminars, workshops, and short courses thereby benefiting people from academia, industry and the military.
