State of Health Estimation of Zinc Air Batteries Using Neural Networks
| Autor: | Daniel Heming, K. T. Kallis, Peter Gloesekoetter, Andre Loechte |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Equivalent series resistance
Computer science 020209 energy 02 engineering and technology 021001 nanoscience & nanotechnology Energy storage Dielectric spectroscopy Nonlinear system Control theory Non-linear least squares 0202 electrical engineering electronic engineering information engineering Equivalent circuit 0210 nano-technology Electrical impedance Energy (signal processing) |
| Zdroj: | Advances in Computational Intelligence ISBN: 9783319591520 IWANN (1) |
| DOI: | 10.1007/978-3-319-59153-7_55 |
| Popis: | One major problem of energy storages is degradation. Degradation leads to a loss of capacity and a higher series resistance. One possibility to determine the state of health is the electrochemical impedance spectroscopy. The ac resistance is therefore measured 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 directly model the state of health in order to speed up the estimation. Zinc air batteries are exemplary used as energy storage, as other problems exists, that can be solved by impedance measurements. Optimizing a cost function is used to determine the fastest combination of examined frequencies. |
| Databáze: | OpenAIRE |
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