Abstract
Refined Instrumental Variable (RIV) estimation is applied to online identify the parameters of the Equivalent Circuit Model (ECM) for Lithium-ion (Li-ion) battery in this paper, which enables accurate parameters estimation with the measurement noise. Since the traditional Recursive Least Squares (RLS) estimation is extremely sensitive to the noise, the parameters in the ECM may fail to converge to their true values under the measurement noise. The RIV estimation is implemented in a bootstrap form, which alternates between the estimation in the system model and the noise model. The Box-Jenkins model of the Li-ion battery transformed from the two RC ECM is selected as the transfer function model for the RIV estimation in this paper. The errors of the two RC ECM are independently generated by the residual of high-order Auto Regressive (AR) model estimation. With the benefit of a series of auxiliary models, the data filtering technology can prefilter the measurement and increase the robustness of the parameters against the noise. Reasonable parameters are possible to be obtained regardless of the noise in the measurement by RIV. Simulation and experimental tests on a LiFePO4 battery validate the efficiency of RIV for parameter online identification compared with traditional RLS.
Highlights
With the continuous decline of the price and the superior performance in the energy density, lithium-ion (Li-ion) battery has become an optimal choice for both the battery pack in the electric vehicle (EV) and the stationary energy storage systems in the grid [1,2,3]
In order to identify reasonable parameters of the battery Equivalent Circuit Model (ECM) considering the effect of the measurement noise, the Refined Instrumental Variable (RIV) estimation is firstly applied to identify the parameters of the two RC ECM online in this paper
E mean value of the identified parameters during the discharging profile and the performance of the two RC ECM are evaluated in Table 4. e Mean Absolute Error (MAE) of the terminal voltage using parameters from RIV is 0.0219 V, and it is 0.0287 V for Recursive Least Squares (RLS). e RMSE of the RIV is lower than using the parameters from RLS. is is mainly because the identified time constants in the two RC ECM are more reasonable in RIV as shown in Table 4. erefore, combined with the results from simulation in the previous section, the advantages of RIV in identifying the parameters under measurement noise are proved compared with RLS
Summary
With the continuous decline of the price and the superior performance in the energy density, lithium-ion (Li-ion) battery has become an optimal choice for both the battery pack in the electric vehicle (EV) and the stationary energy storage systems in the grid [1,2,3]. Look-up table is a possible way to improve the feasibility of the ECM under various conditions, but huge efforts are needed to build the look-up table considering the effect of temperature and SOC Another factor should not be ignored here is that the batteries aging during their operation, which means the constant loop-up table of the parameters from the new cell may fail to accurately predict the characteristic of an old cell. E parameters of the battery ECM with one RC are estimated by the moving window least square method with AutoRegressive (AR) model, but the effect of the window width on the accuracy of identified parameter have not been discussed [35]. In order to identify reasonable parameters of the battery ECM considering the effect of the measurement noise, the Refined Instrumental Variable (RIV) estimation is firstly applied to identify the parameters of the two RC ECM online in this paper.
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