Abstract

The demand for energy storage is increasing massively due to the electrification of transport and the expansion of renewable energies. Current battery technologies cannot satisfy this growing demand as they are difficult to recycle, as the necessary raw materials are mined under precarious conditions, and as the energy density is insufficient. Metal–air batteries offer a high energy density as there is only one active mass inside the cell and the cathodic reaction uses the ambient air. Various metals can be used, but zinc is very promising due to its disposability and non-toxic behavior, and as operation as a secondary cell is possible. Typical characteristics of zinc–air batteries are flat charge and discharge curves. On the one hand, this is an advantage for the subsequent power electronics, which can be optimized for smaller and constant voltage ranges. On the other hand, the state determination of the system becomes more complex, as the voltage level is not sufficient to determine the state of the battery. In this context, electrochemical impedance spectroscopy is a promising candidate as the resulting impedance spectra depend on the state of charge, working point, state of aging, and temperature. Previous approaches require a fixed operating state of the cell while impedance measurements are being performed. In this publication, electrochemical impedance spectroscopy is therefore combined with various machine learning techniques to also determine successfully the state of charge during charging of the cell at non-fixed charging currents.

Highlights

  • The measured EIS spectra are used to fit the parameters of an equivalent circuit that describes the chemical processes of the cell

  • 0 20 40 60 80 100 120 State of Charge [A h]. It has first been shown why a massive expansion of battery technology can be expected in the future

  • The stabilization of the transmission grid can still be maintained with power plants and their rotating masses

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Summary

Benefits of Zinc–Air Batteries

As already explained in the previous section, lithium-ion accumulators are currently used in numerous battery applications. Compared to the lithium–air primary cell, the theoretical specific energy of zinc–air cells is much smaller. In relation to current secondary cell technologies, the theoretical specific energy is still much higher. It should be noted that the zinc–air battery as a secondary cell has not yet been optimized to the extent that lithium-ion technology has. The actual specific energies of current cells are approximately at the level of current lithium-ion cells [20] Another advantage of the zinc–air technology is the low price. While the theoretical energy density of a zinc–air battery is about a factor of 2 greater than that of a lithium-ion cell, the difference in manufacturing costs is much greater. The expected production price of zinc–air in relation to the stored energy is a factor of 10 lower than that of lithium-ion cells. The material price with respect to stored energy and number of cycles is already competitive with lithium-ion technology [21]

Problem
Offset
Current Controller
Drift Compensation
Measurement Data
Adapter Board
Data Generation
Methods
Artificial Neural Networks
Support Vector Regression
Feature Extraction
Regression Using Artificial Neural Networks
Regression Using Support Vector Regression
Conclusions
Full Text
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