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Erschienen in:
2017 | OriginalPaper | Buchkapitel
State of Health Estimation of Zinc Air Batteries Using Neural Networks
verfasst von : Andre Loechte, Daniel Heming, Klaus T. Kallis, Peter Gloesekoetter
Erschienen in: Advances in Computational Intelligence
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Abstract
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.