Abstract
In order to safely and efficiently use the power as well as to extend the lifetime of the traction battery pack, accurate estimation of State of Charge (SoC) is very important and necessary. This paper presents an adaptive observer-based technique for estimating SoC of a lithium-ion battery pack used in an electric vehicle (EV). The RC equivalent circuit model in ADVISOR is applied to simulate the lithium-ion battery pack. The parameters of the battery model as a function of SoC, are identified and optimized using the numerically nonlinear least squares algorithm, based on an experimental data set. By means of the optimized model, an adaptive Luenberger observer is built to estimate online the SoC of the lithium-ion battery pack. The observer gain is adaptively adjusted using a stochastic gradient approach so as to reduce the error between the estimated battery output voltage and the filtered battery terminal voltage measurement. Validation results show that the proposed technique can accurately estimate SoC of the lithium-ion battery pack without a heavy computational load.
Highlights
Hybrid electric vehicles (HEV) and battery electric vehicles (BEV) are playing more and more important roles in improving fuel economy and reducing emissions in public transportation
The first type is nonmodel-based Coulomb counting method used by many HEV/BEV battery management systems [2,3,4]
At each 10% State of Charge (SoC) decrement from 90% to 10%, the numerically nonlinear least squares algorithm is run based on the battery model in equation (1) in Simulink with a ±15% deviation in these initial parameters to find the best fit with the objective to minimize the sum of squares voltage error over the corresponding HPPC profile
Summary
Hybrid electric vehicles (HEV) and battery electric vehicles (BEV) are playing more and more important roles in improving fuel economy and reducing emissions in public transportation. The first type is nonmodel-based Coulomb counting method used by many HEV/BEV battery management systems [2,3,4] This approach samples the battery’s current and computes the accumulated charge to estimate SoC. -SVR model [19], the least squares SVR model [20], the -SVR model [21] and the fuzzy clustering based SVR model [22] This type of method can often produce a good estimate of SoC, due to the powerful ability to approximate nonlinear function surfaces. The observer gain is adaptively adjusted to reduce the mean square error (MSE) between the estimated output voltage and the filtered battery terminal voltage measurement This technique does not require any assumption about the noise characteristics and the specification of the covariance values for the process noise and the measurement noise. Validation results show that this method can provide a good estimate of SoC in electric vehicle driving cycles at a low computational cost
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