Novel Feature Selection Strategy for Cyclic Loss Prediction of Lithium-ion Electric Vehicle Battery

Battery cyclic loss is a key parameter to assess lithium-ion battery degradation in electric vehicles (EVs), while machine learning (ML) methods can be used in evaluating and predicting the degradation trend of battery health due to cyclic loss. The accuracy of ML methods is influenced by the input parameter selection of the model. This paper develops a feature selection strategy based on the utilization of a data pre-processing method, which extracts useful model input parameters from the battery data. To show the advantages of the method, eight widely used ML algorithms are applied to a case study and compared for battery cyclic loss prediction. The results show that the developed feature selection method has improved the prediction accuracy by at least 9%, in the case of LASSO regression The results also depict that the random forest (RF) regression, Gaussian Process Regression (GPR), and XGBoost methods, when applied in combination with the developed feature selection method, show an improvement of 44%, 48% and 52% in the prediction accuracy, respectively.