TY - CONF
A1 - Löchte, Andre
A1 - Gebert, Ole
A1 - Heming, Daniel
A1 - Kallis, Klaus
A1 - Glösekötter, Peter
A2 - Springer, Axel
T1 - State estimation of zinc air batteries using neural networks
T2 - IWANN 2017: Advances in Computational Intelligence
N2 - 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.
Y1 - 2018
UR - https://www.hb.fh-muenster.de/opus4/frontdoor/index/index/docId/10647
SN - 0941-0643
VL - 2018
SP - 1
EP - 9
ER -