The Application of Gaussian Process Regression in State of Health Prediction of Lithium Ion Batteries

Abstract

To predict the available capacity and state of health of lithium ion batteries by using Gaussian process regression, one of the crucial tasks is to choosing the covariance function. This paper proposes a method which can fulfill the Gaussian process regression by using most proper covariance functions or the optimal combination. First, a variety of typical functions are tried to fit the battery experimental data points with least square method, which can give us a valuable interpretation of the properties of the data; next, the three functions with minimum root mean square errors are selected to guide the choosing of the patterns of the covariance functions; then the Gaussian process regression is applied on the training data to determine the ultra-parameters included; finally, we use the Gaussian process model to predict the latter cycle capacities within the test data. Experiments show that the combination of selected covariance functions is effective and can be applied on predictions with different batteries. Also, the method can reduce the time in applying Gaussian process regression by determination of the covariance function quickly.