Estimation of maximum available capacity of lithium-ion battery based on multi-view features extracted from reconstructed charging curve

A new method is developed in the paper to estimate the maximum available capacity which is an important basis for indicating the State of Health (SOH) of lithium-ion batteries. Firstly, a data reconstruction approach is proposed to pre-process the acquired data to suppress the influence of measurement noise and reduce the negative impact on estimation precision when measuring equipment adopts different sampling frequencies. Then, the variation trend of the incremental capacity curve obtained based on the reconstructed data with the battery aging is analyzed, and a health indicator (HI) including multi-view features is put forward to characterize the battery degradation more comprehensively. The multi-view features are coming from the capacity increment curve versus voltage and time, including the maximum value of the capacity increment curve, the voltage corresponding to the maximum value, other values surrounding the maximum value and so on. Finally, Support Vector Regression is used to establish a model between the extracted HI and the maximum available capacity, and two types of open source data are used to verify the performance. The experimental results show that the data reconstruction method and multi-view health indicator proposed in the paper can obtain high precision estimation results. • The problems of ICA in practical application are deeply analyzed. • A data reconstruction method is proposed. • The reconstruction charging voltage curve is monotonically increasing. • The capacity estimation results are not affected by the sampling frequency. • A new HI including multi-view features is put forward.