Estimation of Available Capacity for Lithium-ion Battery Based on Improved Increment Capacity Analysis

It is difficult to estimate the available capacity of lithium-ion battery efficiently and accurately after its decline. To address this problem, an increment capacity analysis method that does not rely on filtering algorithm is proposed to obtain the capacity decline characteristics of different types of batteries, and the estimation model of available capacity is built based on a data-driven approach. First, the shortcomings of low-pass filtering and wavelet filtering in obtaining increment capacity curves are analyzed respectively. Moreover, the patterns of increment capacity curves are compared for differential voltage values at 1, 10, 20, and 50 mV, respectively. Second, the moving variance algorithm is leveraged to evaluate the volatility of the increment capacity curve at different voltage differential values, and the volatility of the voltage value corresponding to the peak position of the curve is evaluated to determine the increment capacity curve with obvious and smooth peak characteristics. The peak value of the curve is extracted as the aging characteristic of the lithium-ion battery and the correlation between the aging characteristic and the aging state of the battery is examined using the Spearman correlation coefficient. Finally, two types of battery aging datasets prepared under different aging test conditions are employed for model validation. The results show that the established estimation model can effectively estimate the available capacity value within the full life cycle. Except for a few values, the relative error of the test results in the two data sets is within 2% for most of the relative error values. In dataset 1, the first 50% of batteries 1 and 3 are selected as training data and the second 50% are selected as test data, respectively, and the absolute error of training results is stable at approximately 0.05 A • h, and the absolute error of test results is approximately 0. 04 A • h. These predictions are made for the full-life cycle discharge capacity values of batteries 2 and 3. The results show that the absolute relative error is restricted within at 2%. In dataset 2, the relative absolute error of the estimation results of the full-life cycle available capacity of batteries 5, 6, and 7 is also less than 0. 1 A • h (2%). The proposed model can make accurate estimations with less than 4% error when measuring the cycle of effective tracking of the capacity recovery phenomenon that occurs during the lithium-ion battery cycle. This result indicates satisfactory robustness and generalization ability. © 2022 Xi'an Highway University. All rights reserved.