Comparison Study on the Battery SoC Estimation with EKF and UKF Algorithms

Energies

Hongwen He¹  Hongzhou Qin²  Xiaokun Sun² 

[1] National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing 100081, China;

关键词: electric vehicles;    dynamic modeling;    SoC estimation;    extended Kalman filter;    unscented Kalman filter;   

DOI  :  10.3390/en6105088

来源: mdpi

【 摘 要 】

The battery state of charge (SoC), whose estimation is one of the basic functions of battery management system (BMS), is a vital input parameter in the energy management and power distribution control of electric vehicles (EVs). In this paper, two methods based on an extended Kalman filter (EKF) and unscented Kalman filter (UKF), respectively, are proposed to estimate the SoC of a lithium-ion battery used in EVs. The lithium-ion battery is modeled with the Thevenin model and the model parameters are identified based on experimental data and validated with the Beijing Driving Cycle. Then space equations used for SoC estimation are established. The SoC estimation results with EKF and UKF are compared in aspects of accuracy and convergence. It is concluded that the two algorithms both perform well, while the UKF algorithm is much better with a faster convergence ability and a higher accuracy.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.