Macromodeling of Microbatteries for IoT Micropower Source Integration

  • Mohammed Shemso Nesro
  • Ibrahim (Abe) M. ElfadelEmail author


Thin-film, solid-state microbatteries represent a viable alternative for powering small form-factor IoT microsystems or storing the power harvested by energy microsensors. One major obstacle to their widespread use in integrated IoT systems has been the absence of a high-fidelity, physics-based, compact model describing their operation and enabling their design and verification in the same CAD environment as integrated power management systems. In this chapter, we develop and validate such models using a thorough analysis of the electrochemistry of a thin-film, solid-state, lithium-ion microbattery. One of our compact models is based on a behavioral linearization step where the nonlinear partial differential equations (PDEs) describing the microbattery electrochemistry are replaced with linear ones without virtually any loss in accuracy. We then apply the well-established methodology of Arnoldi-based model order reduction (MOR) techniques to develop a compact microbattery model capable of reproducing its input-output electrical behavior with less than 1% error with respect to the full nonlinear PDEs. The use of MOR results in more than 30X speedup in transient simulation.


Solid-state microbatteries Micropower sources Li-ion batteries Battery macromodeling 



We would like to acknowledge our SRC industrial liaison, Dr. Lizhong Sun from Applied Materials, for his guidance and input throughout this project. This work was supported by the Semiconductor Research Corporation (SRC) under contract 2012-VJ-2336 with customized funding from the Mubadala Investment Company, Abu Dhabi, UAE.


  1. 1.
    F. Albanoa, A fully integrated microbattery for an implantable microelectromechanical system. J. Power Sources 185, 1524–1532 (2008)CrossRefGoogle Scholar
  2. 2.
    M. Balkanski, Solid-state microbatteries for electronics in the 21st century. Solar Energy Mater. Solar Cells 62, 21–35 (2000)CrossRefGoogle Scholar
  3. 3.
    H.J. Bergveld, W.S. Kruijt, P.H.L. Notten, Electronic network modeling of rechargeable batteries. J. Electrochem. Soc. 145, 3764–3778 (1998)CrossRefGoogle Scholar
  4. 4.
    D. Blaauw et al., A fully integrated microbattery for an implantable micro-electro-mechanical system. J. Power Sources 185, 1524–1532 (2008)CrossRefGoogle Scholar
  5. 5.
    G.G. Botte, V.R. Subramanian, R.E. White, Mathematical modeling of secondary lithium batteries. Electrochim. Acta 45, 2595–2609 (2000)CrossRefGoogle Scholar
  6. 6.
    P. Bouillon et al., Charge/discharge simulation of an all solid-state thin film battery using a one-dimensional model. J. Electrochem. Soc. 159, A104–A115 (2012)Google Scholar
  7. 7.
    D. Danilov, R.A.H. Niessen, P.H.L. Notten, Modeling all-solid-state Li-ion batteries. J. Electrochem. Soc. 58, A215–A222 (2011)CrossRefGoogle Scholar
  8. 8.
    T.S. Dao, C.P. Vyasarayani, J. McPhee, Simplification and order reduction of lithium-ion battery model based on porous-electrode theory. J. Power Sources 198, 329–337 (2012)CrossRefGoogle Scholar
  9. 9.
    N. Dudney, Y. Jang, Analysis of thin-film lithium batteries with cathodes of 50nm to 4um thick LiCoO2. J. Power Sources 22, 119–121 (2003)Google Scholar
  10. 10.
    I.M. Elfadel, D.L. Ling, A block rational Arnoldi algorithm for multipoint passive model-order reduction of multiport RLC networks, in International Conference on Computer Aided-Design (San Jose, CA, 1997), pp. 66–71Google Scholar
  11. 11.
    M.H. Gahaed et al., Circuits for a cubic-millimeter energy-autonomous wireless intraocular pressure monitor. IEEE Trans. Circuits Syst. 60(12), (2013)Google Scholar
  12. 12.
    L.M. Goncalves, J.F. Ribeiro, M.F. Silva, M.M. Silva, J.H. Correia, Integrated solid-state film lithium battery. Procedia Eng. 5, 778–781 (2010)CrossRefGoogle Scholar
  13. 13.
    K. Henrik A. Olsson, J. McPhee, Model order reduction with rational Krylov methods, Doctoral thesis in Numerical Analysis, KTH, Stockholm (2005)Google Scholar
  14. 14.
    K.B. Herbert et al., Switch array system for thin film lithium microbatteries. J. Power Sources 136, 401–407 (2004)CrossRefGoogle Scholar
  15. 15.
  16. 16.
  17. 17.
    H. Jayakumar et al., Powering the internet of things, in Proceedings of the 2014 International Symposium on Low Power Electronics and Design (La Jolla, California, USA, 2014), pp. 75–380Google Scholar
  18. 18.
    M.R. Jongerden, B.R. Haverkort, Which battery model to use? IET Softw. 3(6), 445–457 (2010)CrossRefGoogle Scholar
  19. 19.
    Y. Saad, Iterative Methods for Sparse Linear Systems, 2nd edn. (Society for Industrial and Applied Mathematics, Philadelphia, 2003)CrossRefGoogle Scholar
  20. 20.
    J. Sather, Battery technologies for IoT, in Enabling the Internet of Things, ed. by M. Alioto (Springer, Berlin, 2017)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Mohammed Shemso Nesro
    • 1
  • Ibrahim (Abe) M. Elfadel
    • 1
    Email author
  1. 1.Masdar Institute at Khalifa UniversityAbu DhabiUAE

Personalised recommendations