Comparative Study of the Influence of Open Circuit Voltage Tests on State of Charge Online Estimation for Lithium-Ion Batteries

IEEE Access

卷:8

Yuan Li¹  Meiying Li²  Zhiping Guo²  Hao Guo³  Fei Qi⁴ 

[1] College of Energy and Power Engineering, Inner Mongolia University of Technology, Hohhot, China;

[2] College of Mechanical Engineering, Inner Mongolia University of Technology, Hohhot, China;

[3] Jiangsu Provincial Key Laboratory of Advanced Robotics, Robotics and Microsystems Center, Soochow University, Suzhou, China;

[4] School of Mechanical and Electric Engineering, Soochow University, Suzhou, China;

关键词: Lithium-ion batteries;    state of charge estimation;    open circuit voltage test;    online parameters identification;    adaptive extended Kalman filter algorithm;   

DOI  :  10.1109/ACCESS.2020.2967563

来源: DOAJ

【 摘 要 】

The accurate state of charge (SoC) online estimation is a significant indicator that relates to driving ranges of electric vehicles (EV). The relationship between open circuit voltage (OCV) and SoC plays an important role in SoC estimation for lithium-ion batteries. To compare with the traditional incremental OCV (IO) test and the low current OCV (LO) test, a novel OCV test which combines IO test with LO test (CIL) is proposed in this paper. Based on the reliable parameters online identification of the dual polarization (DP) battery model, two SoC estimation algorithms are compared on the accuracy, robustness and convergence speed for the entire SoC region. Meanwhile, the comparative study of the three OCV-SoC relationships fits by the corresponding OCV tests is discussed in terms of the SoC online estimation under various temperatures. The results show that the adaptive extended Kalman filter (AEKF) algorithm can better improve the accuracy and robustness of SoC estimation than that of the extended Kalman filter (EKF) algorithm. Most importantly, the OCV-SoC relationship obtains from the CIL OCV test method is applied to the AEKF algorithm, which has higher accuracy and better statistical indices of SoC estimation, especially suitable for the low temperature.

【 授权许可】

Unknown