  This paper establishes a novel analytical approach to quantify robustness of
scheduling and battery management for battery supported cyber-physical systems.
A dynamic schedulability test is introduced to determine whether tasks are
schedulable within a finite time window. The test is used to measure robustness
of a real-time scheduling algorithm by evaluating the strength of computing
time perturbations that break schedulability at runtime. Robustness of battery
management is quantified analytically by an adaptive threshold on the state of
charge. The adaptive threshold significantly reduces the false alarm rate for
battery management algorithms to decide when a battery needs to be replaced.
