A score function for state of charge profiles for rechargeable batteries

We propose a new score function to compare and evaluate the relative impact of state-of-charge profiles on overall battery lifetime. Our score function, based on on a discrete Fourier transform of the state-of-charge profile, formalizes and generalizes earlier ideas found in the literature, and can form an important help in optimizing overall life time for battery powered systems. In this paper we introduce and illustrate the method, and discuss its merits as well as open issues and related literature

Mastering operational limitations of LEO satellites – The GOMX-3 approach

When working with space systems the keyword is resources. For a satellite in orbit all resources are sparse and the most critical resource of all is power. It is therefore crucial to have detailed knowledge on how much power is available for an energy harvesting satellite in orbit at every time – especially when in eclipse, where it draws its power from onboard batteries. This paper addresses this problem by a two-step procedure to perform task scheduling for low-earth-orbit (LEO) satellites exploiting formal methods. It combines cost-optimal reachability analyses of priced timed automata networks with a realistic kinetic battery model capable of capturing capacity limits as well as stochastic fluctuations. The procedure is in use for the automatic and resource-optimal day-ahead scheduling of GOMX-3, a power-hungry nanosatellite currently orbiting the earth. We explain how this approach has overcome existing problems, has led to improved designs, and has provided new insights

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.\ud 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\ud introduced Uppaal Stratego tool combines symbolic synthesis with statistical model checking and reinforcement learning to synthesize near-optimal 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

Mastering operational limitations of LEO satellites - The GOMX3 approach

When working with space systems the keyword is resources. For a satellite in orbit all resources are sparse and the most critical resource of all is power. It is therefore crucial to have detailed knowledge on how much power is available for an energy harvesting satellite in orbit at every time – especially when in eclipse, where it draws its power from onboard batteries. This paper addresses this problem by a two-step procedure to perform task scheduling for low-earth-orbit (LEO) satellites exploiting formal methods. It combines cost-optimal reachability analyses of priced timed automata networks with a realistic kinetic battery model capable of capturing capacity limits as well as stochastic fluctuations. The procedure is in use for the automatic and resource-optimal day-ahead scheduling of GomX-3, a power-hungry nanosatellite currently orbiting the earth. We explain how this approach has overcome existing problems, has led to improved designs, and has provided new insights