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.
Highlights
Because of high single-cell voltage, large energy density and long cycle life, lithium-ion batteries are widely used in many fields including electric vehicles, communication stations, and thermometers, etc. [1]–[3]
Based on the above analysis, this paper is to propose a multiple Gaussian process regression (MGPR) method for predicting the SOH of the lithium-ion battery
THE METHOD FRAMEWORK Considering the sufficient utilization of the lithium-ion batteries data, this paper proposes a MGPR method for the SOH prediction of lithium-ion batteries
Summary
Because of high single-cell voltage, large energy density and long cycle life, lithium-ion batteries are widely used in many fields including electric vehicles, communication stations, and thermometers, etc. [1]–[3]. Yang et al [18] built the SOH prediction model by training a three-layer back propagation (BP) neural network These present data-driven methods usually give the deterministic results and do not consider the uncertainty of the predictions [19], [20]. With the development of cloudconnected devices, we can collect a lot of degradation data from the batteries installed on the different units These existing different lithium-ion batteries can provide the important information on SOH prediction. Based on the above analysis, this paper is to propose a multiple Gaussian process regression (MGPR) method for predicting the SOH of the lithium-ion battery. The SOH prediction result of a lithium-ion battery is given by the weighted outputs of multiple GPR models
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