Analytical Program Power Characterization for Battery Depletion-time Estimation

Abstract

Appropriate battery selection is a major design decision regarding the fast growth of battery-operated devices like space rovers, wireless sensor network nodes, rescue robots, and so on. Many such systems are mission critical, where estimation of the battery depletion time has an important role in the design efficiency with regard to the mission time. Accurate characterization of the system power usage pattern is essential for such an estimation. The following complexities exist: (1) The system behavior changes during interaction with the physical world, (2) the power consumption varies as the runtime progresses, (3) the total delivered battery charge has non-linear dependency on the power variability, and (4) design-time exhaustive study about runtime execution paths is almost impossible. This article presents an analytical method to first characterize the power variability of a given embedded program modeled by a directed acyclic graph, concerning the first and the second complexities. To include the third complexity, however, the concept of Worst-case Power Consumption Trace (WPCT) is proposed toward the worst-case scenario in terms of charge depletion for a given battery. A polynomial algorithm is also presented to construct WPCT and use it to estimate a tight lower bound for the system energy depletion time, i.e., its failure time, avoiding an exhaustive study. Comparisons between the analytical and simulation results reveal less than 3.4% of error in the bound estimations for the considered setups.