Battery-Aware Scheduling in Low Orbit: The GomX–3 Case

  • Morten Bisgaard
  • David Gerhardt
  • Holger Hermanns
  • Jan Krčál
  • Gilles Nies
  • Marvin Stenger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9995)

Abstract

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.

References

  1. 1.
  2. 2.
    Alur, R., Dill, D.L.: A theory of timed automata. Theor. Comput. Sci. 126, 183–235 (1994)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Behrmann, G., Fehnker, A., Hune, T., Larsen, K., Pettersson, P., Romijn, J., Vaandrager, F.: Minimum-cost reachability for priced time automata. In: Benedetto, M.D., Sangiovanni-Vincentelli, A. (eds.) HSCC 2001. LNCS, vol. 2034, pp. 147–161. Springer, Heidelberg (2001). doi:10.1007/3-540-45351-2_15 CrossRefGoogle Scholar
  4. 4.
    Behrmann, G., Larsen, K.G., Rasmussen, J.I.: Optimal scheduling using priced timed automata. ACM SIGMETRICS Perform. Eval. Rev. 32(4), 34–40 (2005)CrossRefGoogle Scholar
  5. 5.
    Hermanns, H., Krčál, J., Nies, G.: Recharging probably keeps batteries alive. In: Berger, C., Mousavi, M.R. (eds.) CyPhy 2015. LNCS, vol. 9361, pp. 83–98. Springer, Heidelberg (2015). doi:10.1007/978-3-319-25141-7_7 CrossRefGoogle Scholar
  6. 6.
    Jongerden, M.R., Haverkort, B.R.: Which battery model to use? IET Softw. 3(6), 445–457 (2009). http://dx.doi.org/10.1049/iet-sen.2009.0001
  7. 7.
    Jongerden, M.R.: Model-based energy analysis of battery powered systems. Ph.D. thesis, Enschede. http://doc.utwente.nl/75079/
  8. 8.
    Larsen, K., Behrmann, G., Brinksma, E., Fehnker, A., Hune, T., Pettersson, P., Romijn, J.: As cheap as possible: effcient cost-optimal reachability for priced timed automata. In: Berry, G., Comon, H., Finkel, A. (eds.) CAV 2001. LNCS, vol. 2102, pp. 493–505. Springer, Heidelberg (2001). doi:10.1007/3-540-44585-4_47 CrossRefGoogle Scholar
  9. 9.
    Mader, A., Bohnenkamp, H., Usenko, Y.S., Jansen, D.N., Hurink, J., Hermanns, H.: Synthesis and stochastic assessment of cost-optimal schedules. Int. J. Softw. Tools Technol. Transf. 12(5), 305–318 (2010)CrossRefGoogle Scholar
  10. 10.
    Manwell, J.F., McGowan, J.G.: Lead acid battery storage model for hybrid energy systems. Solar Energy 50(5), 399–405 (1993)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Morten Bisgaard
    • 1
  • David Gerhardt
    • 1
  • Holger Hermanns
    • 2
  • Jan Krčál
    • 2
  • Gilles Nies
    • 2
  • Marvin Stenger
    • 2
  1. 1.GomSpace ApSAalborgDenmark
  2. 2.Saarland University – Computer ScienceSaarbrückenGermany

Personalised recommendations