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Quantification of Battery Depletion Risk Made Efficient

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NASA Formal Methods (NFM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13260))

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Abstract

Rechargeable batteries are the backbone of our mobile and wireless way of life. In the context of model-based battery depletion estimation, the kinetic battery model (KiBaM) pairs modelling convenience with prediction accuracy. This paper proposes algorithms to analyze energy budgets with respect to a rechargeable stochastic KiBaM with capacity bounds. Concretely, we present two different approaches to narrowly bound the cumulative depletion risk induced by a sequence of possibly noisy tasks. One of them enables adaptive discretization of the (provably) relevant portion of the charge space. The other avoids this discretization by instead propagating charge percentiles iteratively, resulting in safe bounds on the depletion risk. Both approaches have their particular strengths with respect to applicability, precision, space and runtime complexity. We provide empirical evidence of their characteristics on the basis of a representative example.

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Acknowledgement

This work was partially supported by ERC Proof of Concept Grant 966770 (LEOpowver), by EU Horizon 2020 Grant 101008233 (MISSION), and by DFG grant 389792660 as part of TRR 248 – CPEC.

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Authors and Affiliations

  1. Universität des Saarlandes, Saarland Informatics Campus, Saarbrücken, Germany

    Holger Hermanns & Gilles Nies

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  1. Holger Hermanns
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  2. Gilles Nies
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Correspondence to Gilles Nies .

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Editors and Affiliations

  1. University of Southern California, Los Angeles, CA, USA

    Jyotirmoy V. Deshmukh

  2. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Klaus Havelund

  3. National Institute of Aerospace, Hampton, VA, USA

    Ivan Perez

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Hermanns, H., Nies, G. (2022). Quantification of Battery Depletion Risk Made Efficient. In: Deshmukh, J.V., Havelund, K., Perez, I. (eds) NASA Formal Methods. NFM 2022. Lecture Notes in Computer Science, vol 13260. Springer, Cham. https://doi.org/10.1007/978-3-031-06773-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-06773-0_8

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  • Online ISBN: 978-3-031-06773-0

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