Details
| Original language | English |
|---|---|
| Article number | 11 |
| Journal | Batteries |
| Volume | 8 |
| Issue number | 2 |
| Publication status | Published - 31 Jan 2022 |
Abstract
Fast-switching semiconductors induce ripple currents on the high-voltage DC bus in the electric vehicle (EV). This paper describes the methods used in the project SiCWell and a new approach to investigate the influence of these overlaid ripples on the battery in EVs. The ripple current generated by the main inverter is demonstrated with a measurement obtained from an electric vehicle. A simulation model is presented which is based on an artificial reference DC bus, according to ISO 21498-2, and uses driving cycles in order to obtain current profiles relevant for battery cycling. A prototype of a battery cycling tester capable of high frequency and precise ripple current generation was developed and is used to cycle cells with superimposed ripple currents within an aging study. To investigate the impact of the frequency and the amplitude of the currents on the battery’s lifetime, these ripple parameters are varied between different test series. Cell parameters such as impedance and capacity are regularly characterized and the aging of the cells is compared to standard DC cycled reference cells. The aging study includes a total of 60 automotive-sized pouch cells. The evaluation of ripple currents and their impact on the battery can improve the state-of-health diagnosis and remaining-useful life prognosis. For the development and validation of such methods, the cycled cells are monitored with a measurement system that regularly measures current and voltage with a sampling rate of 2 MHz. The resulting dataset is suitable for the design of future ripple current aging studies as well as for the development and validation of aging models and methods for battery diagnosis.
Keywords
- Battery aging, Current harmonics, Dataset, Electric vehicle, Lithium-ion batteries (LIBs), Neural network, Ripple current
ASJC Scopus subject areas
- Energy(all)
- Energy Engineering and Power Technology
- Chemistry(all)
- Electrochemistry
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
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In: Batteries, Vol. 8, No. 2, 11, 31.01.2022.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - The Impact of an Overlaid Ripple Current on Battery Aging: The Development of the SiCWell Dataset
AU - Goldammer, Erik
AU - Gentejohann, Marius
AU - Schlüter, Michael
AU - Weber, Daniel
AU - Wondrak, Wolfgang
AU - Dieckerhoff, Sibylle
AU - Gühmann, Clemens
AU - Kowal, Julia
N1 - Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/1/31
Y1 - 2022/1/31
N2 - Fast-switching semiconductors induce ripple currents on the high-voltage DC bus in the electric vehicle (EV). This paper describes the methods used in the project SiCWell and a new approach to investigate the influence of these overlaid ripples on the battery in EVs. The ripple current generated by the main inverter is demonstrated with a measurement obtained from an electric vehicle. A simulation model is presented which is based on an artificial reference DC bus, according to ISO 21498-2, and uses driving cycles in order to obtain current profiles relevant for battery cycling. A prototype of a battery cycling tester capable of high frequency and precise ripple current generation was developed and is used to cycle cells with superimposed ripple currents within an aging study. To investigate the impact of the frequency and the amplitude of the currents on the battery’s lifetime, these ripple parameters are varied between different test series. Cell parameters such as impedance and capacity are regularly characterized and the aging of the cells is compared to standard DC cycled reference cells. The aging study includes a total of 60 automotive-sized pouch cells. The evaluation of ripple currents and their impact on the battery can improve the state-of-health diagnosis and remaining-useful life prognosis. For the development and validation of such methods, the cycled cells are monitored with a measurement system that regularly measures current and voltage with a sampling rate of 2 MHz. The resulting dataset is suitable for the design of future ripple current aging studies as well as for the development and validation of aging models and methods for battery diagnosis.
AB - Fast-switching semiconductors induce ripple currents on the high-voltage DC bus in the electric vehicle (EV). This paper describes the methods used in the project SiCWell and a new approach to investigate the influence of these overlaid ripples on the battery in EVs. The ripple current generated by the main inverter is demonstrated with a measurement obtained from an electric vehicle. A simulation model is presented which is based on an artificial reference DC bus, according to ISO 21498-2, and uses driving cycles in order to obtain current profiles relevant for battery cycling. A prototype of a battery cycling tester capable of high frequency and precise ripple current generation was developed and is used to cycle cells with superimposed ripple currents within an aging study. To investigate the impact of the frequency and the amplitude of the currents on the battery’s lifetime, these ripple parameters are varied between different test series. Cell parameters such as impedance and capacity are regularly characterized and the aging of the cells is compared to standard DC cycled reference cells. The aging study includes a total of 60 automotive-sized pouch cells. The evaluation of ripple currents and their impact on the battery can improve the state-of-health diagnosis and remaining-useful life prognosis. For the development and validation of such methods, the cycled cells are monitored with a measurement system that regularly measures current and voltage with a sampling rate of 2 MHz. The resulting dataset is suitable for the design of future ripple current aging studies as well as for the development and validation of aging models and methods for battery diagnosis.
KW - Battery aging
KW - Current harmonics
KW - Dataset
KW - Electric vehicle
KW - Lithium-ion batteries (LIBs)
KW - Neural network
KW - Ripple current
UR - http://www.scopus.com/inward/record.url?scp=85123755237&partnerID=8YFLogxK
U2 - 10.3390/batteries8020011
DO - 10.3390/batteries8020011
M3 - Article
VL - 8
JO - Batteries
JF - Batteries
IS - 2
M1 - 11
ER -