A Prediction Framework for State of Health of Lithium-Ion Batteries Based on Improved Support Vector Regression

Abstract

As one crucial function of a battery management system (BMS), the state of health (SOH) prediction of a lithium-ion battery is of great significance to safe system operation and the battery’s service life. This paper proposes a framework for SOH prediction, which includes the feature points extraction and SOH prediction. First, based on the incremental capacity (IC) curve, the improved incremental capacity (IIC) curve is deduced by taking the derivative of the IC curve, and the grey relational analysis (GRA) is adopted to select the four feature points with the highest grey relational grade (GRG). Then, an improved sparrow search algorithm (ISSA) is proposed to optimize the parameters of the support vector regression (SVR) for more precise SOH prediction. Finally, experiments are carried out and the results show that the proposed feature points extraction method based on the IC/IIC curves and GRA is efficiently to improve the SOH prediction accuracy. Furthermore, compared with three traditional algorithms, the ISSA-SVR can restrict the SOH prediction error within 1.7%, and it also shows the proposed SOH prediction framework has strong robustness and high universality.

A Novel State-of-Health Prediction and Assessment Strategies for High-Capacity Mining Lithium-Ion Batteries Based on Multi-Indicator

“…42 SVR prediction method.-SVR is an extension of SVM in regression field, which has the advantages of strong generalization ability and fast convergence speed, and has unique advantages in dealing with small sample and nonlinear problems. 43,44 Given a sample set S = {(x 1 , y 1 ), ⋯, (x i , y i ), ⋯, (x N , y N )}, x i ∈ R n , y i ∈ R, where x i and y i are the eigenvector and regression value of the ith sample, respectively. N is the number of samples, and n is the dimension of the input vector.…”