Nonlinear Frequency Response Analysis on Lithium-Ion Batteries: A Model-Based Assessment. Wolff, N., Harting, N., Heinrich, M., Röder, F., & Krewer, U. Electrochimica Acta, 260:614–622, 2018. doi abstract bibtex The nonlinear behavior of electrochemical systems, such as batteries bears essential information on their state and processes interacting within them. A Pseudo-two-Dimensional Lithium-ion battery model is used for Nonlinear Frequency Response Analysis (NFRA). Focus is laid on identification of processes in Lithium-ion batteries. The most commonly applied dynamic electrochemical analysis method, Electrochemical Impedance Spectroscopy (EIS), is limited to linear deflections of the system. This denotes loss of information about nonlinear system behavior. In contrast, NFRA extends this approach to study the nonlinear behavior of the Lithium-ion battery. We show dependency of nonlinear responses on the input amplitude and several model parameters, such as diffusion coefficient, reaction rate constant and double layer capacitance. Parameter variation demonstrates the capability of this method for process identification by investigating the individual higher harmonics and the respective sum. Characteristic peaks can be attributed to electrode reactions and diffusion and frequency regions influenced by the signal can be identified. This work gives a deeper understanding of the nonlinear response of a Lithium-ion battery and as such of how to apply this analysis method for Lithium-ion battery state estimation. It is shown that the method NFRA is essential for reliable process identification. Battery characterization highly benefits from the combination of EIS and NFRA.
@article{wolff_nonlinear_2018-1,
title = {Nonlinear {Frequency} {Response} {Analysis} on {Lithium}-{Ion} {Batteries}: {A} {Model}-{Based} {Assessment}},
volume = {260},
copyright = {All rights reserved},
issn = {00134686},
doi = {10.1016/j.electacta.2017.12.097},
abstract = {The nonlinear behavior of electrochemical systems, such as batteries bears essential information on their state and processes interacting within them. A Pseudo-two-Dimensional Lithium-ion battery model is used for Nonlinear Frequency Response Analysis (NFRA). Focus is laid on identification of processes in Lithium-ion batteries. The most commonly applied dynamic electrochemical analysis method, Electrochemical Impedance Spectroscopy (EIS), is limited to linear deflections of the system. This denotes loss of information about nonlinear system behavior. In contrast, NFRA extends this approach to study the nonlinear behavior of the Lithium-ion battery. We show dependency of nonlinear responses on the input amplitude and several model parameters, such as diffusion coefficient, reaction rate constant and double layer capacitance. Parameter variation demonstrates the capability of this method for process identification by investigating the individual higher harmonics and the respective sum. Characteristic peaks can be attributed to electrode reactions and diffusion and frequency regions influenced by the signal can be identified. This work gives a deeper understanding of the nonlinear response of a Lithium-ion battery and as such of how to apply this analysis method for Lithium-ion battery state estimation. It is shown that the method NFRA is essential for reliable process identification. Battery characterization highly benefits from the combination of EIS and NFRA.},
journal = {Electrochimica Acta},
author = {Wolff, Nicolas and Harting, Nina and Heinrich, Marco and Röder, Fridolin and Krewer, Ulrike},
year = {2018},
keywords = {Dynamic analysis, Electrochemical impedance spectroscopy, Modeling, NFRA},
pages = {614--622},
}
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A Pseudo-two-Dimensional Lithium-ion battery model is used for Nonlinear Frequency Response Analysis (NFRA). Focus is laid on identification of processes in Lithium-ion batteries. The most commonly applied dynamic electrochemical analysis method, Electrochemical Impedance Spectroscopy (EIS), is limited to linear deflections of the system. This denotes loss of information about nonlinear system behavior. In contrast, NFRA extends this approach to study the nonlinear behavior of the Lithium-ion battery. We show dependency of nonlinear responses on the input amplitude and several model parameters, such as diffusion coefficient, reaction rate constant and double layer capacitance. Parameter variation demonstrates the capability of this method for process identification by investigating the individual higher harmonics and the respective sum. Characteristic peaks can be attributed to electrode reactions and diffusion and frequency regions influenced by the signal can be identified. This work gives a deeper understanding of the nonlinear response of a Lithium-ion battery and as such of how to apply this analysis method for Lithium-ion battery state estimation. It is shown that the method NFRA is essential for reliable process identification. 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