Abstract
The open circuit voltage (OCV) and model parameters are critical reference variables for a lithium-ion battery management system estimating the state of charge (SOC) accurately. However, the polarization effect reduces the accuracy of the OCV test, and the model parameters coupled to the polarization voltage increase the non-linearity of the cell model, all challenging SOC estimation. This paper presents an OCV curve fusion method based on the incremental and low-current test. Fusing the incremental test results without polarization effect and the low current test results with non-linear characteristics of electrodes, the fusion method improves the OCV curve’s accuracy. In addition, we design a state observer with model parameters and SOC, and the unscented Kalman filter (UKF) method is employed for co-estimation of SOC and model parameters to eliminate the drift noise effects. The SOC estimation root mean square error (RMSE) of the proposed method achieves 0.99% and 1.67% in the pulse constant current test and dynamic discharge test, respectively. Experimental results and comparisons with other methods highlight the SOC estimation accuracy and robustness of the proposed method.
Highlights
As a lightweight high-energy rechargeable battery, lithium-ion batteries have been widely used in mobile phones, laptops and electric vehicles [1]
It can be seen that compared with the other two methods, the root mean square error (RMSE) of State of charge (SOC) estimation results of the proposed method is reduced by 1.20–1.67% and the mean absolute error (MAE) is reduced by 0.97–1.28% in the pulse constant current discharge test, the RMSE is reduced by 0.82–9.18%, and the MAE is reduced by 0.73–0.85% in the dynamic discharge test
We propose an open circuit voltage (OCV) curve fusion method based on the increment test and low-current test
Summary
As a lightweight high-energy rechargeable battery, lithium-ion batteries have been widely used in mobile phones, laptops and electric vehicles [1]. Given the closed-loop state estimation structure, the model-based observer SOC estimation method is robust This method can be combined with the coulomb integral method, OCV method, and data-driven method to obtain more accurate estimation results. Online parameter identification methods such as dual observer and recursive least squares are used to improve the accuracy of SOC estimation. The fused OCV data are obtained by integrating the first-order backward difference of the low-current test results into the SOC interval corresponding to the incremental test control points, the polarization effect is reduced. OCV curve, we design a state equation including SOC, polarization voltage, and model parameters.
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