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Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model
Journal Contribution - Journal Article
A lithium-ion battery cell’s electrochemical performance can be obtained through a series
of standardized experiments, and the optimal operation and monitoring is performed when a model
of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the
electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization
(DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most
of the studies on battery modeling, attention is paid to the accuracy comparison of the various
ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs
are rarely compared. Hence in this work, three different approaches are performed for a certain
temperature throughout the whole SoC range of the cells for two different load profiles, suitable for
light- and heavy-duty electromotive applications. Analytical equations, least-square-based methods,
and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy,
robustness, and computational time. The influence of the ECMs’ parameter variation on the voltage
root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic,
internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized
and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a
good agreement with the experimental data with less than 3% max voltage relative error and 30mV
RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of
computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time
compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a
positive correlation to the voltage error, depending on the chemistry and loading profile.
of standardized experiments, and the optimal operation and monitoring is performed when a model
of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the
electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization
(DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most
of the studies on battery modeling, attention is paid to the accuracy comparison of the various
ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs
are rarely compared. Hence in this work, three different approaches are performed for a certain
temperature throughout the whole SoC range of the cells for two different load profiles, suitable for
light- and heavy-duty electromotive applications. Analytical equations, least-square-based methods,
and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy,
robustness, and computational time. The influence of the ECMs’ parameter variation on the voltage
root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic,
internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized
and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a
good agreement with the experimental data with less than 3% max voltage relative error and 30mV
RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of
computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time
compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a
positive correlation to the voltage error, depending on the chemistry and loading profile.
Journal: Energies
ISSN: 1996-1073
Issue: 21
Volume: 12
Pages: 1-37
Publication year:2019
CSS-citation score:1
Accessibility:Open