1. Proceedings
  2. I3M
  3. 2019
  4. IMAACA
  5. 10.46354/i3m.2019.imaaca.011

A new proposed shepherd model of a Li-ion open circuit battery based on data fitting

  • a Hanane Hemi  , 
  • b Nacer K M’Sirdi  , 
  • c Aziz Naamane 
  • a,b,cAix Marseille University, CNRS, LIS, SASV, Marseille, France
Cite as
Hemi H., M’Sirdi N. K., Naamane A. (2019). A new proposed shepherd model of a Li-ion open circuit battery based on data fitting. Proceedings of the 12th International Conference on Integrated Modeling and Analysis in Applied Control and Automation (IMAACA 2019), pp. 83-92. DOI: https://doi.org/10.46354/i3m.2019.imaaca.011

Abstract

Disturbances undergone by a complex system can come as much from its external environment as from the internal elements which constitute it. Complex systems are understood in this study as composed of intelligent machines and humans (IMH), and being in charge to accomplish complex tasks in a collaborative way. Performances of these systems, in terms of robustness, adaptation and resilience, strongly depend on the behaviour of the IMH duo. The works that are the subject of this paper focus on the study of the IMH duo and propose a methodological process using jointly cognitive approaches with formal modelling and simulation to analyse, design and control complex systems. For those systems, human beings are necessarily implied in their global behaviour –including stability–, what crucially calls for a better understanding of their behaviour facing diverse complex situations: normal situations, risky situations, critical and accidental situations. Tools and methods proposed by cognitive Sciences, Cognitive Engineering and Knowledge Engineering allow to take into account the different mechanisms involved in human behaviour to enrich complex system models for a better design and control to munimize human errors.

References

  1. A123Systems, (). A123 systems high power lithium ion apr18650m1a datasheet. https://www.
    batteryspace.com/prod-specs/6612.pdf.
  2. Ahmed M., (2017). Modeling lithium-ion battery chargers in plecs
  3. CALCE, (). Center for advanced life cycle engineering. https://web.calce.umd.edu/batteries/
    index.html
  4. Castano S., Gauchia L., Voncila E., and Sanz J., (2015). Dynamical modeling procedure of a li-ion battery pack suitable for real-time applications. Energy Conversion and Management, 92:396–405
  5. Gallo D., Landi C., Luiso M., and Morello R., (2013). Optimization of experimental model parameter identification for energy storage systems. Energies, 6 (9):4572–4590.
  6. He W., Williard N., Chen C., and Pecht M., (2014). State of charge estimation for li-ion batteries using neural network modeling and unscented kalman filter-based error cancellation. International Journal of Electrical Power & Energy Systems, 62:783–791.
  7. knowmade.com, (2017). e-mobility: the new eldorado for li-ion batteries. https://www.knowmade.com/ e-mobility-new-eldorado-li-ion-batteries/.
  8. Li S. and Ke B., (2011). Study of battery modeling using mathematical and circuit oriented approaches. In: Power and Energy Society General Meeting, 2011 IEEE, IEEE, 1–8.
  9. Raszmann E., Baker K., Shi Y., and Christensen D., (2017). Modeling stationary lithium-ion batteries for optimization and predictive control. In: Proceedings of the 2017 IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL, USA, 23–24.
  10. Tian Y., Li D., Tian J., and Xia B., (2017). State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer. Electrochimica Acta, 225:225–234.
  11. Waldmann T., Hogg B.I., and Wohlfahrt-Mehrens M., (2018). Li plating as unwanted side reaction in commercial li-ion cells–a review. Journal of Po Sources, 384:107–124.
  12. Xing Y., He W., Pecht M., and Tsui K.L., (2014). State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Applied Energy, 113:106–115.