Lithium-ion battery state of health monitoring based on ensemble learning

Abstract

State-of-health (SOH) estimation is critical for the battery management system. With autonomous ability and superior nonlinear mapping capability, machine learning is now a hot topic in this field. The training sample set of current machine learning methods based on single learner is often the regional data, which leads to a small range of data acquisition and affects the generalization ability of the model. Regard this issue, the idea of ensemble learning is considered, and by generating differential data samples and synthesizing the output of a series of base learners, a good learning performance can be achieved. Furthermore, gray relational analysis is used for feature correlation analysis. In this paper, NASA battery data sets are used to verify and validate the performance of the proposed method, which indicated an enhanced accuracy of the results based on the ensemble learning method. The proposed battery healthy assessment model based on ensemble learning can be concluded to provide highly accurate and stable SOH predictions.