Abstract
In order to characterize the voltage behavior of a lithium-ion battery for on-board electric vehicle battery management and control applications, a battery model with a moderate complexity was established. The battery open circuit voltage (OCV) as a function of state of charge (SOC) was depicted by the Nernst equation. An equivalent circuit network was adopted to describe the polarization effect of the lithium-ion battery. A linear identifiable formulation of the battery model was derived by discretizing the frequent-domain description of the battery model. The recursive least square algorithm with forgetting was applied to implement the on-line parameter calibration. The validation results show that the on-line calibrated model can accurately predict the dynamic voltage behavior of the lithium-ion battery. The maximum and mean relative errors are 1.666% and 0.01%, respectively, in a hybrid pulse test, while 1.933% and 0.062%, respectively, in a transient power test. The on-line parameter calibration method thereby can ensure that the model possesses an acceptable robustness to varied battery loading profiles.
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Foundation item: Project(50905015) supported by the National Natural Science Foundation of China
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Hu, Xs., Sun, Fc. & Zou, Y. Online model identification of lithium-ion battery for electric vehicles. J. Cent. South Univ. Technol. 18, 1525–1531 (2011). https://doi.org/10.1007/s11771-011-0869-1
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DOI: https://doi.org/10.1007/s11771-011-0869-1