Abstract
States estimations play a vital role in the normal operation of the battery system. However, the existing state estimation methods are not fully applicable to the needs for simplicity, speed, and accuracy in cloud-based data analysis. To solve the above problems, we propose a parameter estimation method based on voltage curve transformation. In this paper, the charging voltage curve is studied for the state-of-charge (SOC) and state-of-health (SOH) estimation. By comparing the voltage curve with an open-circuit voltage (OCV) curve, it is proved that the parameters including initial SOC and capacity have a linear relationship with the curve shape. Furthermore, the dynamic time warping (DTW) algorithm is utilized to visualize the relationship. The data of cells with different state parameters are employed to verify the correction. For implementation, a curve fitting method based on the Least Absolute Deviations (LAD) is adopted to achieve the estimation of state parameters of the battery. Under the condition that SOC range is from 40% to 85%, the root-mean-square error (RMSE) of initial SOC is about 1.6% and the RMSE of SOH is about 2.1%.
Published Version
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