Capacity and Impedance Estimation by Analysing and Modeling in Real Time Incremental Capacity Curves

Mikel Oyarbide, Mikel Arrinda, Denis Sánchez, Haritz Macicior, Paul McGahan, Erik Hoedemaekers and Iosu Cendoya
Additional contact information
Mikel Oyarbide: CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain
Mikel Arrinda: CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain
Denis Sánchez: CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain
Haritz Macicior: CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain
Paul McGahan: Ricardo Automotive and Industrial, Thamova 11-13, 186 00 Prague 8, Czech Republic
Erik Hoedemaekers: TNO, Automotive Campus 30, 5708 JZ Helmond, The Netherlands
Iosu Cendoya: CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain

Energies, 2020, vol. 13, issue 18, 1-18

Abstract: The estimation of lithium ion capacity fade and impedance rise on real application is always a challenging work due to the associated complexity. This work envisages the study of the battery charging profile indicators (CPI) to estimate battery health indicators (capacity and resistance, BHI), for high energy density lithium-ion batteries. Different incremental capacity (IC) parameters of the charging profile will be studied and compared to the battery capacity and resistance, in order to identify the data with the best correlation. In this sense, the constant voltage (CV) step duration, the magnitudes of the IC curve peaks, and the position of these peaks will be studied. Additionally, the behaviour of the IC curve will be modeled to determine if there is any correlation between the IC model parameters and the capacity and resistance. Results show that the developed IC parameter calculation and the correlation strategy are able to evaluate the SOH with less than 1% mean error for capacity and resistance estimation. The algorithm has been implemented on a real battery module and validated on a real platform, emulating heavy duty application conditions. In this preliminary validation, 1% and 3% error has been quantified for capacity and resistance estimation.

Keywords: Li-ion; aging; state of health; incremental capacity; capacity fade; resistance rise (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/18/4855/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/18/4855/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:18:p:4855-:d:414593

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().