Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents
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 condition...
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| Format: | Article |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/1/274 |
| _version_ | 1797499551562596352 |
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| author | Andre Loechte Ignacio Rojas Ruiz Peter Gloesekoetter |
| author_facet | Andre Loechte Ignacio Rojas Ruiz Peter Gloesekoetter |
| author_sort | Andre Loechte |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:49:01Z |
| format | Article |
| id | doaj.art-239e35fefd37488aa220d44b4199bf8d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:49:01Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-239e35fefd37488aa220d44b4199bf8d2023-11-23T11:10:24ZengMDPI AGApplied Sciences2076-34172021-12-0112127410.3390/app12010274Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC CurrentsAndre Loechte0Ignacio Rojas Ruiz1Peter Gloesekoetter2Faculty of Electrical Engineering and Computer Science, University of Applied Sciences Muenster, 48565 Steinfurt, GermanyDepartment of Computer Architecture and Computer Technology, University of Granada, 18071 Granada, SpainFaculty of Electrical Engineering and Computer Science, University of Applied Sciences Muenster, 48565 Steinfurt, GermanyThe 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.https://www.mdpi.com/2076-3417/12/1/274electrochemical impedance spectroscopyartificial neural networkssupport vector regressionzinc-air batterystate estimationstate of charge |
| spellingShingle | Andre Loechte Ignacio Rojas Ruiz Peter Gloesekoetter Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents Applied Sciences electrochemical impedance spectroscopy artificial neural networks support vector regression zinc-air battery state estimation state of charge |
| title | Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents |
| title_full | Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents |
| title_fullStr | Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents |
| title_full_unstemmed | Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents |
| title_short | Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents |
| title_sort | battery state estimation with ann and svr evaluating electrochemical impedance spectra generalizing dc currents |
| topic | electrochemical impedance spectroscopy artificial neural networks support vector regression zinc-air battery state estimation state of charge |
| url | https://www.mdpi.com/2076-3417/12/1/274 |
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