Analytical Solution for Long Battery Lifetime Prediction in Nonadaptive Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11024)


Uppaal SMC is a state-of-the-art tool for modelling and statistical analysis of hybrid systems, allowing the user to directly model the expected battery consumption in battery-operated devices. The tool employs a numerical approach for solving differential equations describing the continuous evolution of a hybrid system, however, the addition of a battery model significantly slows down the simulation and decreases the precision of the analysis. Moreover, Uppaal SMC is not optimized for obtaining simulations with durations of realistic battery lifetimes. We propose an analytical approach to address the performance and precision issues of battery modelling, and a trace extrapolation technique for extending the prediction horizon of Uppaal SMC. Our approach shows a performance gain of up to 80% on two industrial wireless sensor protocol models, while improving the precision with up to 55%. As a proof of concept, we develop a tool prototype where we apply our extrapolation technique for predicting battery lifetimes and show that the expected battery lifetime for several months of device operation can be computed within a reasonable computation time.


Longer Battery Lifetime Nonadaptive System Kinetic Battery Model (KiBaM) LMAC Protocol UPPAAL CORA 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Kevin H. Jørgensen, Niels B. Christoffersen, Rasmus D. Petersen and Tim H. Gjøderum for their help with integrating our tool with VisuAAL and numerous discussions about the annotation of battery modes in Uppaal SMC models of the two wireless network protocols. We thank Neocortec for allowing us to use their MAC protocol in our experiments and Thomas Steen Halkier for discussions about the battery consumption patterns and for providing us with current consumption graphs. The work was funded by the center IDEA4CPS, the Innovation Fund Denmark center DiCyPS and ERC Advanced Grant LASSO. The last author is partially affiliated with FI MU, Brno.


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Hamburg University of TechnologyHamburgGermany
  2. 2.Department of Computer ScienceAalborg UniversityAalborgDenmark

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