Self-discharge prediction method for lithium-ion batteries based on improved support vector machine

An improved support vector regression (SVR) method is proposed for predicting the self-discharge voltage drop (SDV-drop) in lithium-ion batteries. Multiple features were extracted according to the charge and discharge curves of lithium-ion batteries, and the three features having the strongest correlation with the SDV-drop were identified via grey relational analysis. Then, these three features were assigned different weight parameters to obtain composite features which were input into the improved support vector machine through differential evolution algorithm parameter optimization training. Finally, the improved SVR model was obtained. Model training and testing were performed via a battery charge and discharge experiment and battery static experimental data of a new energy vehicle company, and the results indicated that the proposed method had a higher prediction accuracy than the neural-network model and the Gaussian process regression model. • A fast and economical method for self-discharge voltage drop prediction is proposed. • The self-discharge voltage drop is estimated by extracting the features of the charge and discharge curves. • This method can estimate the self-discharge voltage drop and pick out the defective battery. • This method is verified by experiment and simulation with good accuracy.