State of Health Assessment of Lithium-ion Batteries Based on Deep Gaussian Process Regression Considering Heterogeneous Features

Abstract

With the continuous development of the global new energy industry, lithium-ion batteries as new energy and the heart of intelligent manufacturing have attracted much attention. But the performance of lithium-ion battery systems has been deteriorating for a long time. An accurate assessment of its state of health (SOH) can effectively avoid unnecessary loss of lithium-ion batteries due to unexpected failure. Therefore, a method for evaluating the SOH of deep Gaussian process regression (DGPR) lithium-ion batteries based on the Gaussian process and deep network is presented. Firstly, the heterogeneous features reflecting the SOH of lithium-ion batteries were extracted from the aspects of charging and discharging time, the peak of incremental capacity curve (ICC), internal resistance, and energy. Then, through grey correlation analysis (GRA), significant features are introduced into the DGPR model to establish the SOH estimation method for lithium-ion batteries. Finally, the data sets provided by CALCE and NASA are used as an experimental object to compare with different data-driven models to verify the accuracy, reliability and applicability of the proposed models. The experimental results show that the method proposed in this paper has high accuracy in SOH estimation, RMSE is less than 0.7 %, and R2 is more than 98.2 % in each lithium-ion battery. It shows that this method can provide a reliable basis for the health management of lithium-ion batteries.