TY  - CHAP
A1  - Mertens, Konrad
T1  - „String-Dunkelkennlinien: Eine neue effiziente Methode zur Anlagenevaluation“, 33. Symposium Photovoltaische Solarenergie, Staffelstein, 27.04.2018
KW  - Photovoltaik, Solarstrom
Y1  - 2018
ER  - 
TY  - CHAP
A1  - Mertens, K.
T1  - „String-Dunkelkennlinien-Technik zur Diagnose von PV-Anlagen“, Solarkampagne des Kreises Steinfurt, Betriebs- und Sicherheitstagung der DGS,  Berlin, 17.10.2018
Y1  - 2018
ER  - 
TY  - GEN
A1  - Mertens, Konrad
T1  - „Solarstrom auf meinem Dach: Wie geht das? Lohnt sich das noch?“, Solarkampagne des Kreises Steinfurt, Westerkappeln, 25.09.2018
KW  - Photovoltaik, Solarstrom, Speicher
Y1  - 2018
ER  - 
TY  - GEN
A1  - Mertens, Konrad
T1  - „Solarstrom auf meinem Dach: Wie geht das? Lohnt sich das noch?“, Solarkampagne des Kreises Steinfurt, Lengerich, 19.09.2018
KW  - Photovoltaik, Solarstrom, Speicher
Y1  - 2018
ER  - 
TY  - GEN
A1  - Mertens, Konrad
T1  - „Solarstrom auf meinem Dach: Wie geht das? Lohnt sich das noch?“, Solarkampagne des Kreises Steinfurt, Ladbergen, 10.10.2018
KW  - Photovoltaik, Solarstrom, Speicher
Y1  - 2018
ER  - 
TY  - GEN
A1  - Job, Reinhart
T1  - What is our problem with energy and resources, Seminar, 7.11.2018, Center for Interdisciplinary Research in Agribusiness, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brasilien
Y1  - 2018
ER  - 
TY  - JOUR
A1  - Löchte, Andre
A1  - Gebert, Ole
A1  - Heming, Daniel
A1  - Sanders, Tilmann
A1  - Glösekötter, Peter
T1  - State of Charge estimation of zinc air batteries using electrochemical impedance spectroscopy
JF  - Neural Computing and Applications
Y1  - 2018
VL  - 2018
SP  - 1
EP  - 9
PB  - Springer
ER  - 
TY  - CHAP
A1  - Löchte, Andre
A1  - Gebert, Ole
A1  - Horsthemke, Ludwig
A1  - Heming, Daniel
A1  - Glösekötter, Peter
T1  - State of Charge Depended Modeling of an Equivalent Circuit of Zinc Air Batteries Using Electrochemical Impedance Spectroscopy
T2  - International Conference on Time Series and Forecasting: Proceedings of Papers, 19-21 September 2018, Granada (Spain)
N2  - Metal air batteries provide a high energy density as the ca-thodic reaction uses the surrounding air. Different metals can be usedbut zinc is very promising due to its disposability and nontoxic behav-ior.  State  estimation  is  quite  complicated  as  the  voltage  characteristicof  the  battery  is  rather  flat.  Especially  estimating  the  state  of  chargeis important as a secondary electrolysis process during overcharging canlead  to  an  unsafe  state.  Another  technique  for  state  estimation  is  theelectrochemical impedance spectroscopy. Therefore, this paper describesthe process of setup and measuring a time series of impedance spectraat  known  states  of  charge.  Then  these  spectra  are  used  to  derive  anequivalent circuit. Finally the development of the circuit’s parameter areanalyzed to extract most important parameters.
Y1  - 2018
SN  - 978-84-17293-57-4
SP  - 625
EP  - 636
ER  - 
TY  - CHAP
A1  - Löchte, Andre
A1  - Gebert, Ole
A1  - Heming, Daniel
A1  - Kallis, Klaus
A1  - Glösekötter, Peter
ED  - 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
U6  - http://dx.doi.org/10.1007/s00521-018-3705-9
SN  - 0941-0643
VL  - 2018
SP  - 1
EP  - 9
ER  - 
TY  - BOOK
A1  - Poppe, Martin
T1  - Prüfungstrainer Elektrotechnik
Y1  - 2018
SN  - 9783662566480
U6  - http://dx.doi.org/10.1007/978-3-662-56649-7
PB  - Springer
CY  - Heidelberg
ET  - 3
ER  -