@inproceedings{LoechteGebertHorsthemkeetal.2018,
  author    = {L{\"o}chte, Andre and Gebert, Ole and Horsthemke, Ludwig and Heming, Daniel and Gl{\"o}sek{\"o}tter, Peter},
  title     = {State of Charge Depended Modeling of an Equivalent Circuit of Zinc Air Batteries Using Electrochemical Impedance Spectroscopy},
  series = {International Conference on Time Series and Forecasting: Proceedings of Papers, 19-21 September 2018, Granada (Spain)},
  booktitle = {International Conference on Time Series and Forecasting: Proceedings of Papers, 19-21 September 2018, Granada (Spain)},
  isbn      = {978-84-17293-57-4},
  pages     = {625 -- 636},
  year      = {2018},
  abstract  = {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.},
  language  = {de}
}
@inproceedings{LoechteGebertHemingetal.2018,
  author    = {L{\"o}chte, Andre and Gebert, Ole and Heming, Daniel and Kallis, Klaus and Gl{\"o}sek{\"o}tter, Peter},
  title     = {State estimation of zinc air batteries using neural networks},
  series = {IWANN 2017: Advances in Computational Intelligence},
  volume    = {2018},
  booktitle = {IWANN 2017: Advances in Computational Intelligence},
  editor    = {Springer, Axel},
  issn      = {0941-0643},
  doi       = {10.1007/s00521-018-3705-9},
  pages     = {1 -- 9},
  year      = {2018},
  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.},
  language  = {de}
}