Journal
JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY
Volume 18, Issue 5, Pages 1525-1531Publisher
JOURNAL OF CENTRAL SOUTH UNIV TECHNOLOGY
DOI: 10.1007/s11771-011-0869-1
Keywords
battery model; on-line parameter identification; lithium-ion battery; electric vehicle
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Funding
- National Natural Science Foundation of China [50905015]
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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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