Lithium-ion Battery State of Health Monitoring Based on Ensemble Learning

摘要

State of health estimation is critical for 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, by generating differential data samples and synthesizing the output of a series of base learners, a good learning performance can be achieved. Furthermore, grey relational analysis is used for feature correlation analysis. The effectiveness of the proposed method is verified by NASA battery data. Compared with traditional machine learning results, ensemble learning takes better predictive accuracy.