State estimation of zinc air batteries using neural networks

  • 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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Author:Andre Löchte, Ole Gebert, Daniel Heming, Klaus Kallis, Peter Glösekötter
Parent Title (German):IWANN 2017: Advances in Computational Intelligence
Editor:Axel Springer
Document Type:Conference Proceeding
Date of Publication (online):2019/05/20
Date of first Publication:2018/09/28
Provider of the Publication Server:FH Münster - University of Applied Sciences
Release Date:2019/05/20
First Page:1
Last Page:9
Faculties:Elektrotechnik und Informatik (ETI)
Publication list:Glösekötter, Peter
Licence (German):License LogoBibliographische Daten