Your browser does not support JavaScript!

Which battery model to use?

Which battery model to use?

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
IET Software — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The use of mobile devices like cell phones, navigation systems or laptop computers is limited by the lifetime of the included batteries. This lifetime depends naturally on the rate at which energy is consumed; however, it also depends on the usage pattern of the battery. Continuous drawing of a high current results in an excessive drop of residual capacity. However, during intervals with no or very small currents, batteries do recover to a certain extent. The usage pattern of a device can be well modelled with stochastic workload models. However, one still needs a battery model to describe the effects of the power consumption on the state of the battery. Over the years many different types of battery models have been developed for different application areas. In this study we give a detailed analysis of two well-known analytical models, the kinetic battery model (KiBaM) and the so-called diffusion model. We show that the KiBaM is actually an approximation of the more complex diffusion model; this was not known previously. Furthermore, we tested the suitability of these models for performance evaluation purposes, and found that both models are well suited for doing battery lifetime predictions. However, one should not draw conclusions on what is the best usage pattern based on only a few workload traces.


    1. 1)
      • S.C. Hageman . Simple PSpice models let you simulate common battery types. Electron. Design News , 117 - 129
    2. 2)
      • (2005) IEEE Comput..
    3. 3)
      • M. Doyle , T.F. Fuller , J. Newman . Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. J. Electrochem. Soc. , 6 , 1526 - 1533
    4. 4)
      • C. Chiasserini , R. Rao . A model for battery pulsed discharge with recovery effect. Wireless Communications and Networking Conference , 636 - 639
    5. 5)
      • Chiasserini, C., Rao, R.: `Pulsed battery discharge in communication devices', Proc. Fifth Int. Conf. Mobile Computing and Networking, 1999, p. 88–95.
    6. 6)
      • Rakhmatov, D., Vrudhula, S., Wallach, D.A.: `Battery lifetime predictions for energyaware computing', Proc. 2002 Int. Symp. Low Power Electronics and Design (ISLPED '02), 2002, p. 154–159.
    7. 7)
      • Jongerden, M.R., Haverkort, B.R.: `Battery modeling', Technical Report TR-CTIT-08-01, 2008, available at:
    8. 8)
      • Rao, R., Vrudhula, S.B.K., Chang, N.: `Battery optimization vs energy optimization: which to choose and when?', Proc. Int. Conf. Computer Aided Design (ICCAD'05), 2005, p. 439–445.
    9. 9)
      • Cloth, L., Haverkort, B.R., Jongerden, M.R.: `Computing battery lifetime distributions', Proc. 37th Annual IEEE/IFIP Int. Conf. on Dependable Systems and Networks (DSN 2007), 2007, p. 780–789.
    10. 10)
      • (July, 2009) Fortran programs for the simulation of electrochemical systems, available at:
    11. 11)
      • J. Manwell , J. McGowan . Lead acid battery storage model for hybrid energy systems. Solar Energy , 399 - 405
    12. 12)
      • Manwell, J., McGowan, J.: `Extension of the kinetic battery model for wind/hybrid power systems', Proc. fifth European Wind Energy Association Conf. (EWEC '94), 1994, p. 284–289.
    13. 13)
      • Manwell, J., McGowan, J., Baring-Gould, E., S.W., Leotta, A.: `Evaluation of battery models for wind/hybrid power system simulation', Proc. fifth European Wind Energy Association Conference (EWEC '94), 1994, p. 1182–1187.
    14. 14)
      • T.F. Fuller , M. Doyle , J. Newman . Relaxation phenomena in lithium-ion-insertion cells. J. Electrochem. Soc. , 4 , 982 - 990
    15. 15)
      • G. Behrmann , K.G. Larsen , J.I. Rasmussen . Optimal scheduling using priced timed automata. ACM SIGMETRICS Performance Evaluation Rev. , 4 , 34 - 40
    16. 16)
      • S. Gold . A PSPICE macromodel for lithium-ion batteries. 12th Annual Battery Conference on Applications and Advances , 215 - 222
    17. 17)
      • T.F. Fuller , M. Doyle , J. Newman . Simulation and optimization of the dual lithium ion insertion cell. J. Electrochem. Soc. , 1 , 1 - 10
    18. 18)
      • C.-F. Chiasserini , R. Ramesh . Energy efficient battery management. IEEE J. Sel. Areas Commun. , 7 , 1235 - 1245
    19. 19)
      • C. Chiasserini , R. Rao . Improving battery performance by using traffic shaping techniques. IEEE J. Sel. Areas Commun. , 7 , 1385 - 1394
    20. 20)
    21. 21)
      • Rakhmatov, D., Vrudhula, S.: `An analytical high-level battery model for use in energy management of portable electronic systems', Proc. Int. Conf. Computer Aided Design (ICCAD'01), 2001, p. 488–493.
    22. 22)
      • Jongerden, M., Haverkort, B., Bohnenkamp, H., Katoen, J.-P.: `Maximizing system lifetime by battery scheduling', Proc. 39th Annual IEEE/IFIP International Conf. on Dependable Systems and Networks (DSN 2009), IEEE Computer Society Press, 2009, pp. 63–72.

Related content

This is a required field
Please enter a valid email address