A Rapid Capacity Estimation Method for Lithium-ion Batteries Based on Medium-low Frequency Electrochemical Impedance Spectroscopy

For the stepwise utilization of retired batteries in bulk electric vehicles, the technological development of specialized equipment for battery evaluation is in urgent demand, but the development of algorithmic strategies for evaluation software is difficult. The electrochemical impedance spectroscopy (EIS) method that balances speed and accuracy has good feasibility in the implementation of specialized equipment. This paper selects the medium-low frequency EIS that require short testing time and low sampling frequency, which not only saves the cost of high-frequency sampling but also avoids the problem of low-frequency sinusoidal difficult to achieve accurately in the hardware implementation. By fitting the characteristic circle of charge transfer impedance, five health characteristics were extracted, the imaginary part of the vertex, the imaginary part of the inflection point, the abscissa of the circle center, the intersection of the circle with real axis, and its modal decomposition residual value. Pearson correlation coefficient was used to verify the correlation between health characteristics and capacity, and Gaussian process regression index model was used to train and verify the model, realization of rapid estimation of lithium-ion battery capacity. Firstly, the laboratory test data are applied to validate the method, and the experimental values are all within the 95% confidence interval of the estimated values; then the public dataset is applied to further validate the method, which establishes the estimation model with a coefficient of determination of R of 0. 92, and the estimation results in an RMSE of 0. 490 8, and a MAPE of 1. 343 1%. In addition, three methods were selected to reduce the EIS fitting circle data points, to select the impedance at the top and inflection points of the medium-low frequency EIS, to extract the impedance at fixed frequency points in the full frequency band above the real axis, comparing and verifying the accuracy advantages and efficiency of the proposed methods. The results show that by fitting feature circles to extract key parameters as well as fusing inflection point and vertex features, rapid estimation of battery capacity can be achieved while ensuring high accuracy. © 2024 Chang'an University. All rights reserved.