TY - GEN
T1 - Research on SOC estimation of li-ion battery based on adaptive extended Kalman filter
AU - Zhang, Zhengjie
AU - Wang, Mingyue
AU - Cao, Rui
AU - Cheng, Hanchao
AU - Gao, Xinlei
AU - Yang, Shichun
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/5/10
Y1 - 2021/5/10
N2 - The accurate estimation of SOC (State of Charge) is an important prerequisite for the control optimization of electric vehicles, and also a basis for reasonable battery management. In this paper, an 18650 lithium battery based on the RC equivalent model is selected as the research object. An adaptive extended Kalman filter algorithm (AKF) for SOC estimation is proposed, which is used to establish a physical battery cell model in Simscape. The accuracy of the traditional EKF algorithm and AKF algorithm are compared under the 1C constant current discharge experiment and the DST operating experiment. By comparing the simulation results of SOC, it is shown that the AKF algorithm can effectively eliminate the influence of model noise on the estimation, the error is within 2.1%, and it has better convergence and stability, can be applied in the actual use of electric vehicles.
AB - The accurate estimation of SOC (State of Charge) is an important prerequisite for the control optimization of electric vehicles, and also a basis for reasonable battery management. In this paper, an 18650 lithium battery based on the RC equivalent model is selected as the research object. An adaptive extended Kalman filter algorithm (AKF) for SOC estimation is proposed, which is used to establish a physical battery cell model in Simscape. The accuracy of the traditional EKF algorithm and AKF algorithm are compared under the 1C constant current discharge experiment and the DST operating experiment. By comparing the simulation results of SOC, it is shown that the AKF algorithm can effectively eliminate the influence of model noise on the estimation, the error is within 2.1%, and it has better convergence and stability, can be applied in the actual use of electric vehicles.
KW - 18650 battery
KW - Adaptive extended Kalman filter (AKF)
KW - SOC estimation
UR - https://www.scopus.com/pages/publications/85112388656
U2 - 10.1109/ICPS49255.2021.9468248
DO - 10.1109/ICPS49255.2021.9468248
M3 - 会议稿件
AN - SCOPUS:85112388656
T3 - Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2021
SP - 717
EP - 722
BT - Proceedings - 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE International Conference on Industrial Cyber-Physical Systems, ICPS 2021
Y2 - 10 May 2021 through 13 May 2021
ER -