Advertisement

Neural Computing and Applications

, Volume 32, Issue 2, pp 369–377 | Cite as

State estimation of zinc air batteries using neural networks

  • Andre LoechteEmail author
  • Ole Gebert
  • Daniel Heming
  • Klaus T. Kallis
  • Peter Gloesekoetter
S.I. : IWANN2017: Learning algorithms with real world applications
  • 97 Downloads

Abstract

The main task of battery management systems is to keep the working area of the battery in a safe state. Estimation of the state of charge and the state of health is therefore essential. The traditional way uses the voltage level of a battery to determine those values. Modern metal air batteries provide a flat voltage characteristic which necessitates new approaches. One promising technique is the electrochemical impedance spectroscopy, which measures the AC resistance for a set of different frequencies. Previous approaches match the measured impedances with a nonlinear equivalent circuit, which needs a lot of time to solve a nonlinear least-squares problem. This paper combines the electrochemical impedance spectroscopy with neural networks to speed up the state estimation using the example of zinc air batteries. Moreover, these networks are trained with different subsets of the spectra as input data in order to determine the required number of frequencies.

Keywords

Electrochemical impedance spectroscopy State of health State of charge Artificial neural network 

References

  1. 1.
    Pei P, Wang K, Ma Z (2014) Technologies for extending zinc-air battery’s cyclelife: a review. Appl Energy 128:315–324CrossRefGoogle Scholar
  2. 2.
    Energyzer: Zinc-Air application manual. data.energyzer.com. 12 Feb 2017Google Scholar
  3. 3.
    Greenwood NN, Earnshaw A (1988) Chemie der Elemente. 1. Auflage, S. 1545Google Scholar
  4. 4.
    Linden D, Reddy TB (2002) Handbook of batteries, McGraw-Hill, 3rd edn, chapter 13, p 38Google Scholar
  5. 5.
    Electropaedia, battery and energy technologies: battery management systems (BMS). www.mpoweruk.com/bms.htm. 19 Dec 2017
  6. 6.
    Murnane M, Ghazel A (2017) A closer look at state of charge (SOC) and state of health (SOH) estimation techniques for batteries. www.analog.com. 19 Dec 2017
  7. 7.
    Datasheet of lithium ion battery Panasonic NCR18650B. https://na.industrial.panasonic.com. 19 Dec 2017
  8. 8.
    Nernst W (1889) Die elektromotorische Wirksamkeit der Jonen. Zeitschrift für Physikalische Chemie, IV. Band, 6. HeftGoogle Scholar
  9. 9.
    Dominguez D (2013) NASA Glenn safety manual chapter 6—hydrogen. www.nfpa.org. 02 Feb 2013
  10. 10.
    Dambrowski J (2013) Review on methods of state-of-charge estimation with viewpoint to the modern \(LiFePO_4\)/\(Li_4Ti_5O_{12}\) lithium–ion systems. In: International telecommunication energy conference. 35, HamburgGoogle Scholar
  11. 11.
    Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chem Acta 185:1–17CrossRefGoogle Scholar
  12. 12.
    Komijani H, Rezaeihassanabadi S, Parsaei MR, Maleki S (2017) Radial basis function neural network for electrochemical impedance prediction at presence of corrosion inhibitor. Period Polytech Chem Eng 61:128–132Google Scholar
  13. 13.
    Conesa C, Civera JI, Seguí L, Fito P, Laguarda-Miró N (2016) An electrochemical impedance spectroscopy system for monitoring pineapple waste saccharification. Sensors 16(2):188CrossRefGoogle Scholar
  14. 14.
    Arai H, Müller S (2000) AC impedance analysis of bifunctional air electrodes for metal-air-batteries. J Electrochem Soc 147:3584–3591CrossRefGoogle Scholar
  15. 15.
    Drossbach P, Schulz J (1964) Elektrochemische Untersuchungen an Kohleelektroden—Die Überspannung des Wasserstoffs. Electrochim Acta 9:1391–1404CrossRefGoogle Scholar
  16. 16.
    Kiel M (2013) Impedanzspektroskopie an Batterien unter besonderer Berücksichtigung von Batteriesensoren für den Feldeinsatz. Aachener Beiträge des ISEA. 67, Shaker VerlagGoogle Scholar
  17. 17.
    MacKay DJC (1992) A practical Bayesian framework for backpropagation networks. Neural Comput 4(3):448–472CrossRefGoogle Scholar
  18. 18.
    Doan CD, Liong S-Y (2004) Generalization for multilayer neural network Bayesian regularization or early stopping. In: Proceedings of the 2nd conference Asia Pacific Association of hydrology and water resourcesGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering and CSUniversity of Applied Sciences MuensterSteinfurtGermany
  2. 2.Micro- and Nanoelectronic Devices, Faculty of Electrical Engineering and Information TechnologyTU Dortmund UniversityDortmundGermany

Personalised recommendations