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.
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Pei P, Wang K, Ma Z (2014) Technologies for extending zinc-air battery’s cyclelife: a review. Appl Energy 128:315–324
Energyzer: Zinc-Air application manual. data.energyzer.com. 12 Feb 2017
Greenwood NN, Earnshaw A (1988) Chemie der Elemente. 1. Auflage, S. 1545
Linden D, Reddy TB (2002) Handbook of batteries, McGraw-Hill, 3rd edn, chapter 13, p 38
Electropaedia, battery and energy technologies: battery management systems (BMS). www.mpoweruk.com/bms.htm. 19 Dec 2017
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
Datasheet of lithium ion battery Panasonic NCR18650B. https://na.industrial.panasonic.com. 19 Dec 2017
Nernst W (1889) Die elektromotorische Wirksamkeit der Jonen. Zeitschrift für Physikalische Chemie, IV. Band, 6. Heft
Dominguez D (2013) NASA Glenn safety manual chapter 6—hydrogen. www.nfpa.org. 02 Feb 2013
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, Hamburg
Geladi P, Kowalski BR (1986) Partial least-squares regression: a tutorial. Anal Chem Acta 185:1–17
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 61:128–132
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):188
Arai H, Müller S (2000) AC impedance analysis of bifunctional air electrodes for metal-air-batteries. J Electrochem Soc 147:3584–3591
Drossbach P, Schulz J (1964) Elektrochemische Untersuchungen an Kohleelektroden—Die Überspannung des Wasserstoffs. Electrochim Acta 9:1391–1404
Kiel M (2013) Impedanzspektroskopie an Batterien unter besonderer Berücksichtigung von Batteriesensoren für den Feldeinsatz. Aachener Beiträge des ISEA. 67, Shaker Verlag
MacKay DJC (1992) A practical Bayesian framework for backpropagation networks. Neural Comput 4(3):448–472
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 resources
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Loechte, A., Gebert, O., Heming, D. et al. State estimation of zinc air batteries using neural networks. Neural Comput & Applic 32, 369–377 (2020). https://doi.org/10.1007/s00521-018-3705-9
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DOI: https://doi.org/10.1007/s00521-018-3705-9