State-of-Charge Prediction of Battery Management System Based on Principal Component Analysis and Improved Support Vector Machine for Regression

Abstract

State-of-charge (SOC) prediction is an important part of the battery management system (BMS) in electric vehicles. Since external factors (voltage, current, temperature, arrangement of the battery, etc.) impact SOC prediction differently, the SOC is difficult to model. In this article, we apply principal component analysis (PCA) to analyze the contribution of various external factors and propose a new SOC prediction method based on an improved support vector machine for regression (SVR) with data classification and training set size optimization. Three groups of simulation experiments with different inputs based on the original SVR algorithm are conducted in the software ADVISOR, and the simulation results show that the input of three features of the battery (current, voltage and temperature) can satisfy the SOC prediction accuracy. The improved SVR algorithm is then applied to the simulation experiment of the three input features. The proposed method is demonstrated to be faster and more accurate than the original SVR algorithm through a comparison of the simulation results.

Keywords

• State-of-charge (SOC)
• principal component analysis (PCA)
• support vector machine for regression (SVR)
• battery management system (BMS)
• electric vehicles