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

Erik Ramsgaard Wognsen, Boudewijn R.H.M. Haverkort, M.R. Jongerden, René Rydhof Hansen, K.G. Larsen, Sriram Sankaranarayanan (Editor), Enrico Vicario (Editor)

Research output: Other contributionOther research output

Abstract

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 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.
Original languageEnglish
PublisherSpringer Verlag
Number of pages16
Place of PublicationLondon
ISBN (Print)978-3-319-22974-4
DOIs
Publication statusPublished - Sep 2015
Externally publishedYes

Fingerprint

Embedded systems
Life cycle
Scheduling
Wear of materials
Satellites
Secondary batteries
Reinforcement learning
Model checking
Industry
Statistical Models

Keywords

  • EC Grant Agreement nr.: FP7/318490
  • EWI-26287
  • cycle-life battery
  • METIS-314963
  • EC Grant Agreement nr.: FP7/2007-2013
  • Uppaal Stratego
  • IR-98150
  • battery aging

Cite this

Wognsen, E. R., Haverkort, B. R. H. M., Jongerden, M. R., Hansen, R. R., Larsen, K. G., Sankaranarayanan, S. (Ed.), & Vicario, E. (Ed.) (2015, Sep). A Score Function for Optimizing the Cycle-Life of Battery-Powered Embedded Systems. London: Springer Verlag. https://doi.org/10.1007/978-3-319-22975-1_20
Wognsen, Erik Ramsgaard ; Haverkort, Boudewijn R.H.M. ; Jongerden, M.R. ; Hansen, René Rydhof ; Larsen, K.G. ; Sankaranarayanan, Sriram (Editor) ; Vicario, Enrico (Editor). / A Score Function for Optimizing the Cycle-Life of Battery-Powered Embedded Systems. 2015. London : Springer Verlag. 16 p.
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Wognsen, ER, Haverkort, BRHM, Jongerden, MR, Hansen, RR, Larsen, KG, Sankaranarayanan, S (ed.) & Vicario, E (ed.) 2015, A Score Function for Optimizing the Cycle-Life of Battery-Powered Embedded Systems. Springer Verlag, London. https://doi.org/10.1007/978-3-319-22975-1_20

A Score Function for Optimizing the Cycle-Life of Battery-Powered Embedded Systems. / Wognsen, Erik Ramsgaard; Haverkort, Boudewijn R.H.M.; Jongerden, M.R.; Hansen, René Rydhof; Larsen, K.G.; Sankaranarayanan, Sriram (Editor); Vicario, Enrico (Editor).

16 p. London : Springer Verlag. 2015, .

Research output: Other contributionOther research output

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N2 - 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 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.

AB - 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 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.

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Wognsen ER, Haverkort BRHM, Jongerden MR, Hansen RR, Larsen KG, Sankaranarayanan S, (ed.) et al. A Score Function for Optimizing the Cycle-Life of Battery-Powered Embedded Systems. 2015. 16 p. https://doi.org/10.1007/978-3-319-22975-1_20