Robustness Analysis for Battery Supported Cyber-Physical Systems

TL;DR

This paper presents an analytical framework to evaluate and improve the robustness of scheduling and battery management in cyber-physical systems, enhancing reliability and reducing false alarms.

Contribution

It introduces a dynamic schedulability test and an adaptive threshold for battery management, providing novel methods for robustness analysis in these systems.

Findings

The schedulability test effectively measures robustness against time perturbations.

The adaptive threshold reduces false alarms in battery replacement decisions.

Analytical quantification improves system reliability and maintenance accuracy.

Abstract

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.