Robustness Analysis for Battery Supported Cyber-Physical Systems
For designers of battery-supported cyber-physical systems, it provides analytical tools to measure and improve robustness of scheduling and battery management.
This paper introduces a novel analytical approach to quantify robustness of scheduling and battery management in battery-supported cyber-physical systems, including a dynamic schedulability test and an adaptive threshold that reduces false alarm rate for battery replacement decisions.
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.