Online Prediction of Battery Lifetime for Embedded and Mobile Devices

  • Ye Wen
  • Rich Wolski
  • Chandra Krintz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3164)


This paper presents a novel, history-based, statistical technique for online battery lifetime prediction. The approach first takes a one-time, full cycle, voltage measurement of a constant load, and uses it to transform the partial voltage curve of the current workload into a form with robust predictability. Based on the transformed history curve, we apply a statistical method to make a lifetime prediction. We investigate the performance of the implementation of our approach on a widely used mobile device (HP iPAQ) running Linux, and compare it to two similar battery prediction technologies: ACPI and Smart Battery. We employ twenty-two constant and variable workloads to verify the efficacy of our approach. Our results show that this approach is efficient, accurate, and able to adapt to different systems and batteries easily.


Mobile Device Prediction Error Battery Life Reference Curve Voltage Curve 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Benini, L., Castelli, G., Macii, A., Macii, E., Poncino, M., Scarsi, R.: A discrete-time battery model for high-level power estimation. In: Proceedings of Design, Automation and Test in Europe (2000)Google Scholar
  2. 2.
    Compaq, Intel, Microsoft, Phoenix, and Toshiba. Advanced configuration and power interface specification (2002)Google Scholar
  3. 3.
    Danionics lithium-ion polymer battery,
  4. 4.
    Doyle, M., Fuller, T.F., Newman, J.: Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of Electrochem Society 141(1), 1–9 (1994)CrossRefGoogle Scholar
  5. 5.
    Smart Battery System Implementers Forum. Smart battery data specification(v1.1) (1998)Google Scholar
  6. 6.
    Gold, S.: A PSPICE macromodel for lithium-ion batteries. In: Proceedings of Annual Battery Conference on Applications and Advances, pp. 215–222 (1997)Google Scholar
  7. 7.
    Guthaus, M.R., Ringenberg, J.S., Ernst, D., Austin, T.M., Mudge, T., Brown, R.B.: Mibench: A free, commercially representative embedded benchmark suite. In: IEEE 4th Annual Workshop on Workload Characterization, Austin, TX, (December 2001)Google Scholar
  8. 8.
    Intel and Microsoft. Advanced power management(apm) bios interface specification (1996)Google Scholar
  9. 9.
    Linden, D., Reddy, T.B.: Handbook of Batteries, 3rd edn. McGraw-Hill, New York (2002)Google Scholar
  10. 10.
    Linux for handheld devices,
  11. 11.
    Panigrahi, D., Chiasserini, C., Dey, S., Rao, R., Raghunathan, A., Lahiri, K.: Battery life estimation of mobile embedded systems. In: The 14th IEEE International Conference on VLSI Design (2001)Google Scholar
  12. 12.
    Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (2002)zbMATHGoogle Scholar
  13. 13.
    Rakhmatov, D., Vrudhula, S.: Time-to-failure estimation for batteries in portable electronic systems. In: Proceedings of the International Symposium on Low Power Electronics and Design (August 2001)Google Scholar
  14. 14.
    Rakhmatov, D., Vrudhula, S., Wallach, D.A.: Battery lifetime prediction for energy-aware computing. In: Proceedings of the International Symposium on Low Power Electronics and Design (August 2002)Google Scholar
  15. 15.
    Rong, P., Pedram, M.: Remaining battery capacity prediction for lithium-ion batteries. In: Conference of Design Automation and Test in Europe (March 2003)Google Scholar
  16. 16.
    Syracuse, K.C., Clark, W.: A statistical approach to domain performance modeling for oxyhalide primary lithium batteries. In: Proceedings of Annual Battery Conference on Applications and Advances (January 1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ye Wen
    • 1
  • Rich Wolski
    • 1
  • Chandra Krintz
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaSanta Barbara

Personalised recommendations