Impedance spectra classification for determining the state of charge on a lithium iron phosphate cell using a support vector machine

Download statistics - Document (COUNTER):

Jansen, P.; Vergossen, D.; Renner, D.; John, Werner; Götze, J.: Impedance spectra classification for determining the state of charge on a lithium iron phosphate cell using a support vector machine. In: Advances in Radio Science 13 (2015), S. 127-132. DOI: https://doi.org/10.5194/ars-13-127-2015

Repository version

To cite the version in the repository, please use this identifier: https://doi.org/10.15488/1576

Selected time period:

year: 
month: 

Sum total of downloads: 108




Thumbnail
Abstract: 
An alternative method for determining the state of charge (SOC) on lithium iron phosphate cells by impedance spectra classification is given. Methods based on the electric equivalent circuit diagram (ECD), such as the Kalman Filter, the extended Kalman Filter and the state space observer, for instance, have reached their limits for this cell chemistry. The new method resigns on the open circuit voltage curve and the parameters for the electric ECD. Impedance spectra classification is implemented by a Support Vector Machine (SVM). The classes for the SVM-algorithm are represented by all the impedance spectra that correspond to the SOC (the SOC classes) for defined temperature and aging states. A divide and conquer based search algorithm on a binary search tree makes it possible to grade measured impedances using the SVM method. Statistical analysis is used to verify the concept by grading every single impedance from each impedance spectrum corresponding to the SOC by class with different magnitudes of charged error. © 2015 Author(s).
License of this version: CC BY 3.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2015
Appears in Collections:Fakultät für Elektrotechnik und Informatik

distribution of downloads over the selected time period:

downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 83 76.85%
2 image of flag of China China 8 7.41%
3 image of flag of United States United States 5 4.63%
4 image of flag of Brazil Brazil 4 3.70%
5 image of flag of Austria Austria 3 2.78%
6 image of flag of Canada Canada 2 1.85%
7 image of flag of Netherlands Netherlands 1 0.93%
8 image of flag of Czech Republic Czech Republic 1 0.93%
9 image of flag of Australia Australia 1 0.93%

Further download figures and rankings:


Hinweis

Zur Erhebung der Downloadstatistiken kommen entsprechend dem „COUNTER Code of Practice for e-Resources“ international anerkannte Regeln und Normen zur Anwendung. COUNTER ist eine internationale Non-Profit-Organisation, in der Bibliotheksverbände, Datenbankanbieter und Verlage gemeinsam an Standards zur Erhebung, Speicherung und Verarbeitung von Nutzungsdaten elektronischer Ressourcen arbeiten, welche so Objektivität und Vergleichbarkeit gewährleisten sollen. Es werden hierbei ausschließlich Zugriffe auf die entsprechenden Volltexte ausgewertet, keine Aufrufe der Website an sich.

Search the repository


Browse