A Score Function for Optimizing the Cycle-Life of Battery-Powered Embedded Systems

An ever increasing share of embedded systems is powered by rechargeable batteries. These batteries deteriorate with the number of charge/discharge cycles they are subjected to, the so-called cycle life. In this paper, we propose the wear score function to compare and evaluate the relative impact of usage (charge and discharge) profiles on cycle life. The wear score function can not only be used to rank different usage profiles, these rankings can also be used as a criterion for optimizing the overall lifetime of a battery-powered system. We perform such an optimization on a nano-satellite case study provided by the company GomSpace. The scheduling of the system is modelled as a network of (stochastic) weighted timed games. In a stochastic setting, exact optimization is very expensive. However, the recently introduced Uppaal Stratego tool combines symbolic synthesis with statistical model checking and reinforcement learning to synthesize nearoptimal scheduling strategies subject to possible hard timing-constaints. We use this to study the trade-off between optimal short-term dynamic payload selection and the operational life of the satellite.