State-of-Health Prediction for Lithium-Ion Batteries with Multiple Gaussian Process Regression Model

Abstract

State-of-health (SOH) prediction for lithium-ion batteries is a challenging and important topic in the modern industry. With the advent of cloud-connected devices, there are huge amounts of the battery degradation trend data available. How to make full use of these existing degradation data for the SOH prediction is a valuable problem deserving deep research. Aiming at this problem, a multiple Gaussian process regression (MGPR) method is proposed for the SOH prediction of lithium-ion batteries. In this work, the health indicators (HIs) are firstly extracted from the charging process curves of the batteries, and the mutual information analysis is used to select the important HIs which are strongly correlated to the SOH. These selected HIs are applied as the regression model input for describing the aging procedure of the battery effectively. Then, Gaussian process regression modeling is performed on the different batteries to bring multiple GPR models. Lastly, a weighting strategy based on the prediction uncertainty is designed to integrate the predictions from the multiple GPR models. The method validations are executed on the battery datasets from NASA, and the results show that the proposed MGPR method has higher prediction accuracy than the basic GPR method.